subject:(decision trees)

Univerzitet u Beogradu

1. Jovanović, Miloš, 1982-. Аутоматско генерисање алгоритама стабала одлучивања за класификацију.

Degree: Fakultet organizacionih nauka, 2016, Univerzitet u Beogradu

URL: https://fedorabg.bg.ac.rs/fedora/get/o:12050/bdef:Content/get

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Организационе науке - Моделирање пословних система и пословно одлучивање / Organizational sciences - Business system and business decision making

Стабла одлучивања су врло распрострањен модел… (more)

Subjects/Keywords: decision trees; algorithm; components; metaheuristics

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APA (6^th Edition):

Jovanović, Miloš, 1. (2016). Аутоматско генерисање алгоритама стабала одлучивања за класификацију. (Thesis). Univerzitet u Beogradu. Retrieved from https://fedorabg.bg.ac.rs/fedora/get/o:12050/bdef:Content/get

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Jovanović, Miloš, 1982-. “Аутоматско генерисање алгоритама стабала одлучивања за класификацију.” 2016. Thesis, Univerzitet u Beogradu. Accessed January 23, 2021. https://fedorabg.bg.ac.rs/fedora/get/o:12050/bdef:Content/get.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Jovanović, Miloš, 1982-. “Аутоматско генерисање алгоритама стабала одлучивања за класификацију.” 2016. Web. 23 Jan 2021.

Vancouver:

Jovanović, Miloš 1. Аутоматско генерисање алгоритама стабала одлучивања за класификацију. [Internet] [Thesis]. Univerzitet u Beogradu; 2016. [cited 2021 Jan 23]. Available from: https://fedorabg.bg.ac.rs/fedora/get/o:12050/bdef:Content/get.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Jovanović, Miloš 1. Аутоматско генерисање алгоритама стабала одлучивања за класификацију. [Thesis]. Univerzitet u Beogradu; 2016. Available from: https://fedorabg.bg.ac.rs/fedora/get/o:12050/bdef:Content/get

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Central Connecticut State University

2. Tadigadapa, Sairam L. Y., 1969-. The Application of Decision Trees for Diagnosing Liver Disease.

Degree: Department of Mathematical Sciences, 2014, Central Connecticut State University

URL: http://content.library.ccsu.edu/u?/ccsutheses,1993

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Liver disease is a major problem in India and many parts of the world. This thesis analyzed the performance of the C5.0, CHAID, QUEST, and… (more)

Subjects/Keywords: Decision trees.; Liver – Diseases – Diagnosis.

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APA (6^th Edition):

Tadigadapa, Sairam L. Y., 1. (2014). The Application of Decision Trees for Diagnosing Liver Disease. (Thesis). Central Connecticut State University. Retrieved from http://content.library.ccsu.edu/u?/ccsutheses,1993

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Tadigadapa, Sairam L. Y., 1969-. “The Application of Decision Trees for Diagnosing Liver Disease.” 2014. Thesis, Central Connecticut State University. Accessed January 23, 2021. http://content.library.ccsu.edu/u?/ccsutheses,1993.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Tadigadapa, Sairam L. Y., 1969-. “The Application of Decision Trees for Diagnosing Liver Disease.” 2014. Web. 23 Jan 2021.

Vancouver:

Tadigadapa, Sairam L. Y. 1. The Application of Decision Trees for Diagnosing Liver Disease. [Internet] [Thesis]. Central Connecticut State University; 2014. [cited 2021 Jan 23]. Available from: http://content.library.ccsu.edu/u?/ccsutheses,1993.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Tadigadapa, Sairam L. Y. 1. The Application of Decision Trees for Diagnosing Liver Disease. [Thesis]. Central Connecticut State University; 2014. Available from: http://content.library.ccsu.edu/u?/ccsutheses,1993

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Delft University of Technology

3. Bao, Shiwei (author). A Robust Solution to Train Shunting using Decision Trees.

Degree: 2018, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:9acb3bd1-1ffb-4ebb-b886-f10c7291b101

► This research tackles the Train Unit Shunting Problem (TUSP) in train maintenance service sites. Many researches focus on producing feasible solutions, but only a few… (more)

Subjects/Keywords: train shunting; decision trees; Robust

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APA (6^th Edition):

Bao, S. (. (2018). A Robust Solution to Train Shunting using Decision Trees. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:9acb3bd1-1ffb-4ebb-b886-f10c7291b101

Chicago Manual of Style (16^th Edition):

Bao, Shiwei (author). “A Robust Solution to Train Shunting using Decision Trees.” 2018. Masters Thesis, Delft University of Technology. Accessed January 23, 2021. http://resolver.tudelft.nl/uuid:9acb3bd1-1ffb-4ebb-b886-f10c7291b101.

MLA Handbook (7^th Edition):

Bao, Shiwei (author). “A Robust Solution to Train Shunting using Decision Trees.” 2018. Web. 23 Jan 2021.

Vancouver:

Bao S(. A Robust Solution to Train Shunting using Decision Trees. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Jan 23]. Available from: http://resolver.tudelft.nl/uuid:9acb3bd1-1ffb-4ebb-b886-f10c7291b101.

Council of Science Editors:

Bao S(. A Robust Solution to Train Shunting using Decision Trees. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:9acb3bd1-1ffb-4ebb-b886-f10c7291b101

University of Texas – Austin

4. Si, Si, Ph.D. Large-scale non-linear prediction with applications.

Degree: PhD, Computer science, 2016, University of Texas – Austin

URL: http://hdl.handle.net/2152/43583

► With an immense growth in data, there is a great need for training and testing machine learning models on very large data sets. Several standard… (more)

▼ With an immense growth in data, there is a great need for training and testing machine learning models on very large data sets. Several standard non-linear algorithms based on either kernels (e.g., kernel support vector machines and kernel ridge regression) or decision trees (e.g., gradient boosted decision trees) often yield superior predictive performance on various machine learning tasks compared to linear methods; however, they suffer from severe computation and memory challenges when scaling to millions of data instances. To overcome these challenges, we develop a family of scalable kernel-approximation-based and decision-tree-based algorithms to reduce the computational cost of non linear methods in terms of training time, prediction time and memory usage. We further show their superior performance on a wide range of machine learning tasks including large-scale classification, regression, and extreme multi-label learning. In particular, we make the following contributions: (1) We develop a family of memory efficient kernel approximation algorithms by exploiting the structure of kernel matrices. The proposed kernel approximation scheme can significantly speed up the training phase of kernel machines; (2) We make the connection between forming a kernel approximation and predicting new instances using kernel machines, and propose a series of improvements over the classical \Nystrom kernel approximation method. We show that these improvements result in an order of magnitude speed-up in prediction time on large-scale classification and regression tasks with millions of training instances; (3) We overcome the challenges of applying decision trees to the extreme multi-label classification problem, which can have more than 100,000 different labels, and develop the first Gradient Boosting Decision Tree (GBDT) algorithm for extreme multi-label learning. We show that the modified GBDT algorithm achieves substantial reductions in prediction time and model size. Advisors/Committee Members: Dhillon, Inderjit S. (advisor), Grauman, Kristen (committee member), Keerthi, Selvaraj Sathiya (committee member), Mooney, Raymond (committee member).

Subjects/Keywords: Kernel methods; Classification; Decision trees

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APA (6^th Edition):

Si, Si, P. D. (2016). Large-scale non-linear prediction with applications. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/43583

Chicago Manual of Style (16^th Edition):

Si, Si, Ph D. “Large-scale non-linear prediction with applications.” 2016. Doctoral Dissertation, University of Texas – Austin. Accessed January 23, 2021. http://hdl.handle.net/2152/43583.

MLA Handbook (7^th Edition):

Si, Si, Ph D. “Large-scale non-linear prediction with applications.” 2016. Web. 23 Jan 2021.

Vancouver:

Si, Si PD. Large-scale non-linear prediction with applications. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2016. [cited 2021 Jan 23]. Available from: http://hdl.handle.net/2152/43583.

Council of Science Editors:

Si, Si PD. Large-scale non-linear prediction with applications. [Doctoral Dissertation]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/43583

5. Njoku, Obinna Chilezie. Decision Trees and Their Application for Classification and Regression Problems.

Degree: MSin Mathematics, Mathematics, 2019, Missouri State University

URL: https://bearworks.missouristate.edu/theses/3406

► Tree methods are some of the best and most commonly used methods in the field of statistical learning. They are widely used in classification… (more)

Subjects/Keywords: decision trees; classification trees; regression trees; bagging; random forest; boosting; Statistical Models

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APA (6^th Edition):

Njoku, O. C. (2019). Decision Trees and Their Application for Classification and Regression Problems. (Masters Thesis). Missouri State University. Retrieved from https://bearworks.missouristate.edu/theses/3406

Chicago Manual of Style (16^th Edition):

Njoku, Obinna Chilezie. “Decision Trees and Their Application for Classification and Regression Problems.” 2019. Masters Thesis, Missouri State University. Accessed January 23, 2021. https://bearworks.missouristate.edu/theses/3406.

MLA Handbook (7^th Edition):

Njoku, Obinna Chilezie. “Decision Trees and Their Application for Classification and Regression Problems.” 2019. Web. 23 Jan 2021.

Vancouver:

Njoku OC. Decision Trees and Their Application for Classification and Regression Problems. [Internet] [Masters thesis]. Missouri State University; 2019. [cited 2021 Jan 23]. Available from: https://bearworks.missouristate.edu/theses/3406.

Council of Science Editors:

Njoku OC. Decision Trees and Their Application for Classification and Regression Problems. [Masters Thesis]. Missouri State University; 2019. Available from: https://bearworks.missouristate.edu/theses/3406

Delft University of Technology

6. Vos, Daniël (author). Adversarially Robust Decision Trees Against User-Specified Threat Models.

Degree: 2020, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:c9d9cdc6-4f98-4730-8fb6-43e6e3444002

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In the present day we use machine learning for sensitive tasks that require models to be both understandable and robust. Although traditional models such as… (more)

Subjects/Keywords: Adversarial Machine Learning; Decision Trees; Cyber Security

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Vos, D. (. (2020). Adversarially Robust Decision Trees Against User-Specified Threat Models. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:c9d9cdc6-4f98-4730-8fb6-43e6e3444002

Chicago Manual of Style (16^th Edition):

Vos, Daniël (author). “Adversarially Robust Decision Trees Against User-Specified Threat Models.” 2020. Masters Thesis, Delft University of Technology. Accessed January 23, 2021. http://resolver.tudelft.nl/uuid:c9d9cdc6-4f98-4730-8fb6-43e6e3444002.

MLA Handbook (7^th Edition):

Vos, Daniël (author). “Adversarially Robust Decision Trees Against User-Specified Threat Models.” 2020. Web. 23 Jan 2021.

Vancouver:

Vos D(. Adversarially Robust Decision Trees Against User-Specified Threat Models. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2021 Jan 23]. Available from: http://resolver.tudelft.nl/uuid:c9d9cdc6-4f98-4730-8fb6-43e6e3444002.

Council of Science Editors:

Vos D(. Adversarially Robust Decision Trees Against User-Specified Threat Models. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:c9d9cdc6-4f98-4730-8fb6-43e6e3444002

Delft University of Technology

7. Buijs, Cas (author). Improving the robustness of decision trees in security-sensitive setting.

Degree: 2020, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:7fa236fe-686a-423b-ad56-5ac81e07d129

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Machine learning is used for security purposes, to differ between the benign and the malicious. Where decision trees can lead to understandable and explainable classifications,… (more)

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Machine learning is used for security purposes, to differ between the benign and the malicious. Where decision trees can lead to understandable and explainable classifications, an adversary could manipulate the model input to evade detection, e.g. the malicious been classified as the benign. State-of-the-art techniques improve the robustness by taking these adversarial attacks into account when building the model. In this work, I identify three factors contributing to the robustness of a decision tree: feature frequency, shortest distance between malicious leaves and benign prediction space, and impurity of benign prediction space. I propose two splitting criteria to improve these factors and suggest a combination with two trade-off approaches to balance the use of these splitting criteria with a common splitting criterion, Gini Impurity, in order to balance accuracy and robustness. These combinations allow building robuster models against adversaries manipulating the malicious data without considering adversarial attacks. The approaches are evaluated in a white-box setting against a decision tree and random forest, considering an unbounded adversary where robustness is measured using a L1-distance norm and the false negative rate. All combinations lead to robuster models at different costs in terms of accuracy, showing that adversarial attacks do not need to be taken into account to improve robustness. Compared to state-of-the-art work, the best approach achieves on average 3.17% better accuracy with an on average lower robustness of 5.5% on the used datasets for a single decision tree. In a random forest the best approach achieves on average 2.87% better robustness with a 2.37% better accuracy on the used datasets compared to the state-of-the-art work. The state-of-the-art work does not seem to affect all of the identified factors, which leaves room for even robuster models than currently existing.

Computer Science | Cyber Security

Advisors/Committee Members: Verwer, S.E. (mentor), Lagendijk, R.L. (graduation committee), Tax, D.M.J. (graduation committee), Delft University of Technology (degree granting institution).

Subjects/Keywords: Adversarial Machine Learning; Decision Trees; Robust learning

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APA (6^th Edition):

Buijs, C. (. (2020). Improving the robustness of decision trees in security-sensitive setting. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:7fa236fe-686a-423b-ad56-5ac81e07d129

Chicago Manual of Style (16^th Edition):

Buijs, Cas (author). “Improving the robustness of decision trees in security-sensitive setting.” 2020. Masters Thesis, Delft University of Technology. Accessed January 23, 2021. http://resolver.tudelft.nl/uuid:7fa236fe-686a-423b-ad56-5ac81e07d129.

MLA Handbook (7^th Edition):

Buijs, Cas (author). “Improving the robustness of decision trees in security-sensitive setting.” 2020. Web. 23 Jan 2021.

Vancouver:

Buijs C(. Improving the robustness of decision trees in security-sensitive setting. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2021 Jan 23]. Available from: http://resolver.tudelft.nl/uuid:7fa236fe-686a-423b-ad56-5ac81e07d129.

Council of Science Editors:

Buijs C(. Improving the robustness of decision trees in security-sensitive setting. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:7fa236fe-686a-423b-ad56-5ac81e07d129

Columbia University

8. Zhu, Huichen. Robust Statistical Approaches Dealing with High-Dimensional Observational Data.

Degree: 2019, Columbia University

URL: https://doi.org/10.7916/d8-2a44-2470

► The theme of this dissertation is to develop robust statistical approaches for the high-dimensional observational data. The development of technology makes data sets more accessible… (more)

▼ The theme of this dissertation is to develop robust statistical approaches for the high-dimensional observational data. The development of technology makes data sets more accessible than any other time in history. Abundant data leads to numerous appealing findings and at the same time, requires more thoughtful efforts. We are encountered many obstacles when dealing with high-dimensional data. Heterogeneity and complex interaction structure rule out the traditional mean regression method and expect a novel approach to circumvent the complexity and obtain significant conclusions. Missing data mechanism in high-dimensional data is complicated and is hard to manage with existing methods. This dissertation contains three parts to tackle these obstacles: (1) a tree-based method integrated with the domain knowledge to improve prediction accuracy; (2) a tree-based method with linear splits to accommodate the large-scale and highly correlated data set; (3) an integrative analysis method to reduce the dimension and impute the block-wise missing data simultaneously. In the first part of the dissertation, we propose a tree-based method called conditional quantile random forest (CQRF) to improve the screening and intervention of the onset of mentor disorder incorporating with rich and comprehensive electronic medical records (EMR). Our research is motivated by the REactions to Acute Care and Hospitalization (REACH) study, which is an ongoing prospective observational cohort study of the patient with symptoms of a suspected acute coronary syndrome (ACS). We aim to develop a robust and effective statistical prediction method. The proposed approach fully takes the population heterogeneity into account. We partition the sample space guided by quantile regression over the entire quantile process. The proposed CQRF can provide a more comprehensive and accurate prediction. We also provide theoretical justification for the estimate quantile process. In the second part of the dissertation, we apply the proposed CQRF to REACH data set. The predictive analysis derived by the proposed approach shows that for both entire samples and high-risk group, the proposed CQRF provides more accurate predictions compared with other existing and widely used methods. The variable importance scores give a promising result based on the proposed CQRF that the proposed importance scores identify two variables which have been proved to be critical features by the qualitative study. We also apply the proposed CQRF to Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study data set. We show that the proposed approach improves the personalized medicine recommendation compared with existing treatment recommendation method. We also conduct two simulation studies based on the two real data sets. Both simulation studies validate the consistent property of the estimated quantile process. In the second part, we also extend the proposed CQRF with univariate splits to linear splits to accommodate a large number of highly correlated…

Subjects/Keywords: Biometry; Biometry – Data processing; Statistics; Decision trees

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APA (6^th Edition):

Zhu, H. (2019). Robust Statistical Approaches Dealing with High-Dimensional Observational Data. (Doctoral Dissertation). Columbia University. Retrieved from https://doi.org/10.7916/d8-2a44-2470

Chicago Manual of Style (16^th Edition):

Zhu, Huichen. “Robust Statistical Approaches Dealing with High-Dimensional Observational Data.” 2019. Doctoral Dissertation, Columbia University. Accessed January 23, 2021. https://doi.org/10.7916/d8-2a44-2470.

MLA Handbook (7^th Edition):

Zhu, Huichen. “Robust Statistical Approaches Dealing with High-Dimensional Observational Data.” 2019. Web. 23 Jan 2021.

Vancouver:

Zhu H. Robust Statistical Approaches Dealing with High-Dimensional Observational Data. [Internet] [Doctoral dissertation]. Columbia University; 2019. [cited 2021 Jan 23]. Available from: https://doi.org/10.7916/d8-2a44-2470.

Council of Science Editors:

Zhu H. Robust Statistical Approaches Dealing with High-Dimensional Observational Data. [Doctoral Dissertation]. Columbia University; 2019. Available from: https://doi.org/10.7916/d8-2a44-2470

Central Connecticut State University

9. Sambasivan, Rajiv, 1971-. Modeling of Flight Delays.

Degree: Department of Mathematical Sciences, 2012, Central Connecticut State University

URL: http://content.library.ccsu.edu/u?/ccsutheses,1817

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This thesis develops models for flight delays by major airlines at domestic destinations. Federal regulations require airlines to meet specific revenue criteria regarding their on-time… (more)

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This thesis develops models for flight delays by major airlines at domestic destinations. Federal regulations require airlines to meet specific revenue criteria regarding their on-time arrival performance, which includes the cause of delay (if any). Causes of delay include National Aviation System (NAS) delay, Carrier delay and Late Aircraft delay. The number of records of the on-time performance dataset for a single year in recent times has exceeded five million records and poses challenges to develop comprehensive models of flight delays. This study develops such models by using sampling techniques and summarized views of the data. The large individual flight dataset was sampled to ensure that delays at all domestic destinations by all carriers are represented. Monthly performance summaries published by the BTS was the other dataset used to develop models for this study. The CART (Classification and Regression Trees) algorithm was used to develop the models for this study. The CART algorithm is non-parametric and does not impose specific restrictions on the distribution of errors from the model. This enhances the robustness of the results. This thesis used both the classification and regression aspects of the algorithm. The classification aspect of the algorithm was used to develop the models that predicted the on-time arrival or delay (arrival delay of greater than fifteen minutes) of individual flights. The regression tree aspect of the algorithm was used to develop models that predicted the proportion of total delays and delays from specific causes. Results of the modeling include the following. The extent of departure delays is the most important predictor of arrival delays. NAS delays are the most important predictor of departure delays. The particular airline is the most important predictor of service performance to a particular destination. There is a seasonal aspect to delays, that is, summer and winter months are associated with higher delays. Finally, delays for airlines are dominated by specific causes (NAS delays, Carrier Delays or Late Aircraft Delays). Future work should explore finding better predictors of delay, detailed analysis of interesting subsets uncovered by this study and alternative algorithms for model development.

"Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science in Data Mining."; Thesis advisor: Daniel T. Larose.; M.S.,Central Connecticut State University,,2012.;

Advisors/Committee Members: Larose, Daniel T..

Subjects/Keywords: Decision trees.; Flight delays – United States.

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APA (6^th Edition):

Sambasivan, Rajiv, 1. (2012). Modeling of Flight Delays. (Thesis). Central Connecticut State University. Retrieved from http://content.library.ccsu.edu/u?/ccsutheses,1817

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Sambasivan, Rajiv, 1971-. “Modeling of Flight Delays.” 2012. Thesis, Central Connecticut State University. Accessed January 23, 2021. http://content.library.ccsu.edu/u?/ccsutheses,1817.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Sambasivan, Rajiv, 1971-. “Modeling of Flight Delays.” 2012. Web. 23 Jan 2021.

Vancouver:

Sambasivan, Rajiv 1. Modeling of Flight Delays. [Internet] [Thesis]. Central Connecticut State University; 2012. [cited 2021 Jan 23]. Available from: http://content.library.ccsu.edu/u?/ccsutheses,1817.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Sambasivan, Rajiv 1. Modeling of Flight Delays. [Thesis]. Central Connecticut State University; 2012. Available from: http://content.library.ccsu.edu/u?/ccsutheses,1817

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Central Connecticut State University

10. Bitiukov, Alex. Implementation of Customer Lifetime Value model in the context of Financial Services.

Degree: Department of Mathematical Sciences, 2014, Central Connecticut State University

URL: http://content.library.ccsu.edu/u?/ccsutheses,2047

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The challenge of justifying long-term investments using traditional business case evaluation methodologies became increasingly transparent due to increased awareness, shareholder activism, traditional and social media.… (more)

Subjects/Keywords: Financial services industry.; Markov processes.; Decision trees.

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APA (6^th Edition):

Bitiukov, A. (2014). Implementation of Customer Lifetime Value model in the context of Financial Services. (Thesis). Central Connecticut State University. Retrieved from http://content.library.ccsu.edu/u?/ccsutheses,2047

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Bitiukov, Alex. “Implementation of Customer Lifetime Value model in the context of Financial Services.” 2014. Thesis, Central Connecticut State University. Accessed January 23, 2021. http://content.library.ccsu.edu/u?/ccsutheses,2047.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Bitiukov, Alex. “Implementation of Customer Lifetime Value model in the context of Financial Services.” 2014. Web. 23 Jan 2021.

Vancouver:

Bitiukov A. Implementation of Customer Lifetime Value model in the context of Financial Services. [Internet] [Thesis]. Central Connecticut State University; 2014. [cited 2021 Jan 23]. Available from: http://content.library.ccsu.edu/u?/ccsutheses,2047.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Bitiukov A. Implementation of Customer Lifetime Value model in the context of Financial Services. [Thesis]. Central Connecticut State University; 2014. Available from: http://content.library.ccsu.edu/u?/ccsutheses,2047

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Notre Dame

11. Patrick J. Miller. Boosted Decision Trees for Multivariate, Hierarchically Clustered, and Longitudinal Data</h1>.

Degree: Psychology, 2017, University of Notre Dame

URL: https://curate.nd.edu/show/qz20sq89x8q

► The problem of finding structure in big data sets is becoming increasingly relevant to psychologists as it becomes easier and cheaper to collect data… (more)

▼ The problem of finding structure in big data sets is becoming increasingly relevant to psychologists as it becomes easier and cheaper to collect data on human behavior. This dissertation focuses on the problem of identifying important structural features like main effects, nonlinear effects, and interactions in big data sets when the number of predictors is large. In general, this goal can be referred to as exploratory regression analysis. Exploratory regression analysis is beneficial because the results suggest testable hypotheses, can limit the number of plausible models, and help avoid errors in model specification. Exploratory regression analysis is usually carried out using basic data visualization techniques, simple statistical models, or by fitting a number of parametric models and selecting the best from among them. However, these procedures can require strong assumptions and may not be feasible when the number of predictors is large. Gradient tree boosting (friedman_greedy_2001) is a promising alternative for exploratory regression analysis because it builds an interpretable model that approximates nonlinear effects and interactions among predictors without a priori specification. However, it is not clear how to build and interpret gradient tree boosting models in the context of multivariate, longitudinal, and hierarchically clustered data commonly found in psychological research. This dissertation develops two procedures for estimating gradient tree boosting models for multivariate, longitudinal and hierarchically clustered data. Multivariate tree boosting selects predictors that explain covariance in multiple outcomes. Mixed effects tree boosting takes hierarchically clustered data into account by treating a grouping variable as random. Longitudinal data can be modeled in boosted decision trees by including time as a candidate for splitting in mixed effects tree boosting. These procedures are illustrated by application to real data. Simulations demonstrate that the methods balance true and false positive rates when selecting variables, and achieve low prediction error at sample and effect sizes commonly observed in psychology. Advisors/Committee Members: Gitta Lubke, Research Director.

Subjects/Keywords: nonparametric regression; nonlinear; Boosted decision trees

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Miller, P. J. (2017). Boosted Decision Trees for Multivariate, Hierarchically Clustered, and Longitudinal Data</h1>. (Thesis). University of Notre Dame. Retrieved from https://curate.nd.edu/show/qz20sq89x8q

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Miller, Patrick J.. “Boosted Decision Trees for Multivariate, Hierarchically Clustered, and Longitudinal Data</h1>.” 2017. Thesis, University of Notre Dame. Accessed January 23, 2021. https://curate.nd.edu/show/qz20sq89x8q.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Miller, Patrick J.. “Boosted Decision Trees for Multivariate, Hierarchically Clustered, and Longitudinal Data</h1>.” 2017. Web. 23 Jan 2021.

Vancouver:

Miller PJ. Boosted Decision Trees for Multivariate, Hierarchically Clustered, and Longitudinal Data</h1>. [Internet] [Thesis]. University of Notre Dame; 2017. [cited 2021 Jan 23]. Available from: https://curate.nd.edu/show/qz20sq89x8q.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Miller PJ. Boosted Decision Trees for Multivariate, Hierarchically Clustered, and Longitudinal Data</h1>. [Thesis]. University of Notre Dame; 2017. Available from: https://curate.nd.edu/show/qz20sq89x8q

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Waterloo

12. Li, Chengbo. Causal Sensitivity Analysis for Decision Trees.

Degree: 2015, University of Waterloo

URL: http://hdl.handle.net/10012/9076

► Ventilator assignments in the pediatric intensive care unit (PICU) are made by medical experts; however, for some patients the relationship between ventilator assignment and patient… (more)

▼ Ventilator assignments in the pediatric intensive care unit (PICU) are made by medical experts; however, for some patients the relationship between ventilator assignment and patient health status is not well understood. Using observational data collected by Virtual PICU Systems (VPS) (58,772 PICU visits with covariates and different ventilator assignments conducted by clinicians), we attempt to identify which patients would derive the greatest clinical benefit from ventilators by providing a concise model to help clinicians estimate a ventilator's potential effect on individual patients, in the event that patients need to be prioritized due to limited ventilator availability. Effectively allocating ventilators requires estimating the effect of ventilation on different patients; this is known as individual treatment effect estimation. However, we only have access to non-randomized data, which is confounded by the fact that sicker patients are more likely to be ventilated. In order to reduce bias due to potential confounding to estimate the average treatment effect, propensity score matching has been widely studied and applied to estimate the average treatment effect, which matches patients from treated group with patients from control group based on similar conditional probability of ventilator assignment given an individual patient's features. This matching process assumes no unmeasured confounding, meaning there must be no unobserved covariates influencing both treatment assignment and patient's outcome. However, this is not guaranteed to be true, and if it is not, the average treatment effect estimation using propensity score matching approach can be fragile given an unmeasured confounder with strong influences. Rosenbaum and Dual Sensitivity Analysis is specifically designed for potential unmeasured confounder problems in propensity score matching, assuming confounder's existence it evaluates how "sensitive" the treatment effect estimation after matching can be. This sensitivity analysis method has been well-studied to evaluate the estimated average treatment effect based on propensity score matching, specifically, using generalized linear models as the propensity score model. However, both estimating treatment effect via propensity score matching and its sensitivity analysis have their limitations: first, propensity score matching only helps in estimating the average treatment effect, while it does not provide much information about individual treatment effect on each patient; second, Rosenbaum and Dual Sensitivity Analysis only evaluates the robustness of estimated average treatment effect from propensity score matching, while it cannot evaluate the robustness of a complex model estimating the individual treatment effect, such as a decision tree model. To solve this problem, we attempt to estimate the individual treatment effect from observational study, by proposing the treatment effect tree (TET) model. TET can be estimated through learning a Node-Level-Stabilizing decision tree based on matched…

Subjects/Keywords: Treatment Effect; Sensitivity Analysis; Decision Trees

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Li, C. (2015). Causal Sensitivity Analysis for Decision Trees. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/9076

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Li, Chengbo. “Causal Sensitivity Analysis for Decision Trees.” 2015. Thesis, University of Waterloo. Accessed January 23, 2021. http://hdl.handle.net/10012/9076.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Li, Chengbo. “Causal Sensitivity Analysis for Decision Trees.” 2015. Web. 23 Jan 2021.

Vancouver:

Li C. Causal Sensitivity Analysis for Decision Trees. [Internet] [Thesis]. University of Waterloo; 2015. [cited 2021 Jan 23]. Available from: http://hdl.handle.net/10012/9076.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Li C. Causal Sensitivity Analysis for Decision Trees. [Thesis]. University of Waterloo; 2015. Available from: http://hdl.handle.net/10012/9076

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Georgia

13. Cheng, Xiao. The rise of the Big Data.

Degree: 2014, University of Georgia

URL: http://hdl.handle.net/10724/29730

► Big Data has been the new trend in businesses. As technology advances, the ability to collect large amount of data and learn insights from them… (more)

Subjects/Keywords: Big Data; Machine Learning; Decision Trees

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Cheng, X. (2014). The rise of the Big Data. (Thesis). University of Georgia. Retrieved from http://hdl.handle.net/10724/29730

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Cheng, Xiao. “The rise of the Big Data.” 2014. Thesis, University of Georgia. Accessed January 23, 2021. http://hdl.handle.net/10724/29730.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Cheng, Xiao. “The rise of the Big Data.” 2014. Web. 23 Jan 2021.

Vancouver:

Cheng X. The rise of the Big Data. [Internet] [Thesis]. University of Georgia; 2014. [cited 2021 Jan 23]. Available from: http://hdl.handle.net/10724/29730.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Cheng X. The rise of the Big Data. [Thesis]. University of Georgia; 2014. Available from: http://hdl.handle.net/10724/29730

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

14. Asiri, Maha Mohammed. A transfer learning-based feature reduction method to improve classification accuracy.

Degree: 2017, NC Docks

URL: http://libres.uncg.edu/ir/uncg/f/Asiri_uncg_0154M_12397.pdf

► The need for efficient data use grows in machine learning algorithm for dataset with larger feature sets. Feature selection is the process of selecting minimum… (more)

Subjects/Keywords: Machine learning; Random graphs; Decision trees; Algorithms

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Asiri, M. M. (2017). A transfer learning-based feature reduction method to improve classification accuracy. (Thesis). NC Docks. Retrieved from http://libres.uncg.edu/ir/uncg/f/Asiri_uncg_0154M_12397.pdf

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Asiri, Maha Mohammed. “A transfer learning-based feature reduction method to improve classification accuracy.” 2017. Thesis, NC Docks. Accessed January 23, 2021. http://libres.uncg.edu/ir/uncg/f/Asiri_uncg_0154M_12397.pdf.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Asiri, Maha Mohammed. “A transfer learning-based feature reduction method to improve classification accuracy.” 2017. Web. 23 Jan 2021.

Vancouver:

Asiri MM. A transfer learning-based feature reduction method to improve classification accuracy. [Internet] [Thesis]. NC Docks; 2017. [cited 2021 Jan 23]. Available from: http://libres.uncg.edu/ir/uncg/f/Asiri_uncg_0154M_12397.pdf.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Asiri MM. A transfer learning-based feature reduction method to improve classification accuracy. [Thesis]. NC Docks; 2017. Available from: http://libres.uncg.edu/ir/uncg/f/Asiri_uncg_0154M_12397.pdf

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Columbia University

15. McNellis, Ryan Thomas. Training Decision Trees for Optimal Decision-Making.

Degree: 2020, Columbia University

URL: https://doi.org/10.7916/d8-7zzq-gx78

► Many analytics problems in Operations Research and the Management Sciences can be framed as decision-making problems containing uncertain input parameters to be estimated from data.… (more)

▼ Many analytics problems in Operations Research and the Management Sciences can be framed as decision-making problems containing uncertain input parameters to be estimated from data. For example, inventory optimization problems often require forecasts of future demand, and product recommendation systems (e.g., movies, sporting goods) depend on models for predicting customer responses to the feasible recommendations. Therefore, a question central to many analytics problems is how to optimally build models from data which estimate the uncertain inputs for the decision problems of interest. We argue that most common approaches for this task either (a) focus on the wrong objectives in training the models for the decision problem, or (b) focus on the right objectives but only study how to do so with prohibitively simple machine learning models (e.g. linear and logistic regression). In this work, we study how to train decision tree models for predicting uncertain parameters for analytical decision-making problems. Unlike other machine learning models such as linear and logistic regression, decision trees are both nonparameteric and interpretable, allowing them the capability of modeling highly complex relationships between data and predictions while also being easily visualized and interpreted. We propose tractable algorithms for decision tree training in the context of three problem domains relevant to Operations Research. First, we study how to train decision trees for delivering real-time personalized recommendations of products in settings where little prior data is available for training purposes. This problem is known in the literature as the contextual bandit problem and requires careful navigation of the so-called "exploration-exploitation trade-off" in utilizing the decision tree models. Second, we propose a new framework which we call Market Segmentation Trees (MSTs) for training decision tree models for the purposes of market segmentation and personalization. We explore several applications of MSTs relevant to personalized advertising, including recommending hotels to Expedia users as a function of their search queries and segmenting ad auctions according to the distribution of bids that they receive. Finally, we propose a general framework for training decision tree models for uncertain optimization problems which we call "SPO Trees" (SPOTs). In contrast to the typical objective of maximizing predictive accuracy, the SPOT framework trains decision trees to maximize the quality of the solutions found in the uncertain optimization problem, therefore yielding better decisions in several analytics problems of interest.

Subjects/Keywords: Operations research; Statistics; Mathematics; Decision making; Decision trees

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

McNellis, R. T. (2020). Training Decision Trees for Optimal Decision-Making. (Doctoral Dissertation). Columbia University. Retrieved from https://doi.org/10.7916/d8-7zzq-gx78

Chicago Manual of Style (16^th Edition):

McNellis, Ryan Thomas. “Training Decision Trees for Optimal Decision-Making.” 2020. Doctoral Dissertation, Columbia University. Accessed January 23, 2021. https://doi.org/10.7916/d8-7zzq-gx78.

MLA Handbook (7^th Edition):

McNellis, Ryan Thomas. “Training Decision Trees for Optimal Decision-Making.” 2020. Web. 23 Jan 2021.

Vancouver:

McNellis RT. Training Decision Trees for Optimal Decision-Making. [Internet] [Doctoral dissertation]. Columbia University; 2020. [cited 2021 Jan 23]. Available from: https://doi.org/10.7916/d8-7zzq-gx78.

Council of Science Editors:

McNellis RT. Training Decision Trees for Optimal Decision-Making. [Doctoral Dissertation]. Columbia University; 2020. Available from: https://doi.org/10.7916/d8-7zzq-gx78

KTH

16. Svantesson, David. Implementing Streaming Parallel Decision Trees on Graphic Processing Units.

Degree: Electrical Engineering and Computer Science (EECS), 2018, KTH

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230953

►

Decision trees have long been a prevalent area within machine learning. With streaming data environments as well as large datasets becoming increasingly common, researchers… (more)

▼

Decision trees have long been a prevalent area within machine learning. With streaming data environments as well as large datasets becoming increasingly common, researchers have developed decision tree algorithms adapted to streaming data. One such algorithm is SPDT, which approaches the streaming data problem by making use of workers on a network combined with a dynamic histogram approximation of the data. There exist several implementations for decision trees on GPU, but those are uncommon in a streaming data setting. In this research, conducted at RISE SICS, the possibilities of accelerating the SPDT algorithm on GPU is investigated. An implementation is successfully created using the CUDA platform. The implementation uses a set number of data samples per layer to better fit the GPU platform. Experiments were conducted to investigate the impact on both accuracy and speed. It is found that the GPU implementation performs as well as the CPU implementation in terms of accuracy, suggesting that using small subsets of the data in each layer is sufficient for making accurate split decisions. The GPU implementation is found to be up to 113 times faster than the reference Scala CPU implementation for one of the tested datasets, and 13 times faster on average over all the tested datasets. Weak parts of the implementation are identified, and further improvements are suggested to increase both accuracy and runtime performance.

Beslutsträd har länge varit ett betydande område inom maskininlärning. Strömmandedata och stora dataset har blivit allt vanligare, vilket har lett till att forskare utvecklat algoritmer för beslutsträd anpassade till dessa miljöer. En av dessa algoritmer är SPDT. Denna algoritm använder sig av flera arbetare i ett nätverk kombinerat med en dynamisk histogram-representation av data. Det existerar flera implementationer av beslutsträd på grafikkort, men inte många för strömmande data. I detta forskningsarbete, utfört på RISE SICS, undersöks möjligheten att snabba upp SPDT genom att accelerera beräkningar med hjälp av grafikkort. En lyckad implementation skriven i CUDA beskrivs. Implementationen anpassar sig till grafikkortsplattformen genom att använda sig utav ett bestämt antal datapunkter per lager. Experiment som undersöker effekten på noggrannhet och hastighet har genomförts. Resultaten visar att GPU-implementationen presterar lika väl som CPU-implementationen vad gäller noggrannhet, vilket påvisar att användandet av en mindre del av data i varje lager är tillräckligt för goda resultat. GPU-implementationen är upp till 113 gånger snabbare jämfört med en existerande CPU-implementation skriven i Scala, och är i medel 13 gånger snabbare. Svagheter i implementationen identifieras, och vidare förbättringar till implementationen föreslås för att förbättra både noggrannhet och hastighetsprestanda.

Subjects/Keywords: decision trees; streaming; gpu; hpc; spdt; streaming parallel decision trees; machine learning; Computer Sciences; Datavetenskap (datalogi)

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Svantesson, D. (2018). Implementing Streaming Parallel Decision Trees on Graphic Processing Units. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230953

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Svantesson, David. “Implementing Streaming Parallel Decision Trees on Graphic Processing Units.” 2018. Thesis, KTH. Accessed January 23, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230953.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Svantesson, David. “Implementing Streaming Parallel Decision Trees on Graphic Processing Units.” 2018. Web. 23 Jan 2021.

Vancouver:

Svantesson D. Implementing Streaming Parallel Decision Trees on Graphic Processing Units. [Internet] [Thesis]. KTH; 2018. [cited 2021 Jan 23]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230953.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Svantesson D. Implementing Streaming Parallel Decision Trees on Graphic Processing Units. [Thesis]. KTH; 2018. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230953

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Louisville

17. Alvanpour, Aneseh. Uncovering exceptional predictions using exploratory analysis of second stage machine learning.

Degree: MS, 2017, University of Louisville

URL: 10.18297/etd/2651 ; https://ir.library.louisville.edu/etd/2651

► Nowadays, algorithmic systems for making decisions are widely used to facilitate decisions in a variety of fields such as medicine, banking, applying for universities… (more)

Subjects/Keywords: machine learning; interpretability; decision trees; classification; exceptional predictions; decision rules; Other Computer Engineering

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Alvanpour, A. (2017). Uncovering exceptional predictions using exploratory analysis of second stage machine learning. (Masters Thesis). University of Louisville. Retrieved from 10.18297/etd/2651 ; https://ir.library.louisville.edu/etd/2651

Chicago Manual of Style (16^th Edition):

Alvanpour, Aneseh. “Uncovering exceptional predictions using exploratory analysis of second stage machine learning.” 2017. Masters Thesis, University of Louisville. Accessed January 23, 2021. 10.18297/etd/2651 ; https://ir.library.louisville.edu/etd/2651.

MLA Handbook (7^th Edition):

Alvanpour, Aneseh. “Uncovering exceptional predictions using exploratory analysis of second stage machine learning.” 2017. Web. 23 Jan 2021.

Vancouver:

Alvanpour A. Uncovering exceptional predictions using exploratory analysis of second stage machine learning. [Internet] [Masters thesis]. University of Louisville; 2017. [cited 2021 Jan 23]. Available from: 10.18297/etd/2651 ; https://ir.library.louisville.edu/etd/2651.

Council of Science Editors:

Alvanpour A. Uncovering exceptional predictions using exploratory analysis of second stage machine learning. [Masters Thesis]. University of Louisville; 2017. Available from: 10.18297/etd/2651 ; https://ir.library.louisville.edu/etd/2651

Texas State University – San Marcos

18. Phillips, Clark Raymond. Employing an Efficient and Scalable Implementation of the Cost Sensitive Alternating Decision Tree algorithm to Efficiently Link Person Records.

Degree: MS, Computer Science, 2015, Texas State University – San Marcos

URL: https://digital.library.txstate.edu/handle/10877/5576

► When collecting person records for census, identifying individuals accurately is paramount. Over time, people change their phone numbers, their addresses, even their names. Without a… (more)

Subjects/Keywords: Decision trees; Machine learning; Alternating decision tree; Computer science – Mathematics; Combinatorial analysis

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Phillips, C. R. (2015). Employing an Efficient and Scalable Implementation of the Cost Sensitive Alternating Decision Tree algorithm to Efficiently Link Person Records. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/5576

Chicago Manual of Style (16^th Edition):

Phillips, Clark Raymond. “Employing an Efficient and Scalable Implementation of the Cost Sensitive Alternating Decision Tree algorithm to Efficiently Link Person Records.” 2015. Masters Thesis, Texas State University – San Marcos. Accessed January 23, 2021. https://digital.library.txstate.edu/handle/10877/5576.

MLA Handbook (7^th Edition):

Phillips, Clark Raymond. “Employing an Efficient and Scalable Implementation of the Cost Sensitive Alternating Decision Tree algorithm to Efficiently Link Person Records.” 2015. Web. 23 Jan 2021.

Vancouver:

Phillips CR. Employing an Efficient and Scalable Implementation of the Cost Sensitive Alternating Decision Tree algorithm to Efficiently Link Person Records. [Internet] [Masters thesis]. Texas State University – San Marcos; 2015. [cited 2021 Jan 23]. Available from: https://digital.library.txstate.edu/handle/10877/5576.

Council of Science Editors:

Phillips CR. Employing an Efficient and Scalable Implementation of the Cost Sensitive Alternating Decision Tree algorithm to Efficiently Link Person Records. [Masters Thesis]. Texas State University – San Marcos; 2015. Available from: https://digital.library.txstate.edu/handle/10877/5576

University of Hong Kong

19. Tsang, Pui-kwan, Smith. Efficient decision tree building algorithms for uncertain data.

Degree: 2008, University of Hong Kong

URL: http://hdl.handle.net/10722/50806

Subjects/Keywords: Decision trees.; Data mining.; Algorithms.

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Tsang, Pui-kwan, S. (2008). Efficient decision tree building algorithms for uncertain data. (Thesis). University of Hong Kong. Retrieved from http://hdl.handle.net/10722/50806

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Tsang, Pui-kwan, Smith. “Efficient decision tree building algorithms for uncertain data.” 2008. Thesis, University of Hong Kong. Accessed January 23, 2021. http://hdl.handle.net/10722/50806.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Tsang, Pui-kwan, Smith. “Efficient decision tree building algorithms for uncertain data.” 2008. Web. 23 Jan 2021.

Vancouver:

Tsang, Pui-kwan S. Efficient decision tree building algorithms for uncertain data. [Internet] [Thesis]. University of Hong Kong; 2008. [cited 2021 Jan 23]. Available from: http://hdl.handle.net/10722/50806.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Tsang, Pui-kwan S. Efficient decision tree building algorithms for uncertain data. [Thesis]. University of Hong Kong; 2008. Available from: http://hdl.handle.net/10722/50806

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

20. Basgalupp, Márcio Porto. LEGAL-Tree: um algoritmo genético multi-objetivo para indução de árvores de decisão.

Degree: PhD, Ciências de Computação e Matemática Computacional, 2010, University of São Paulo

URL: http://www.teses.usp.br/teses/disponiveis/55/55134/tde-12052010-165344/ ;

►

Dentre as diversas tarefas em que os algoritmos evolutivos têm sido empregados, a indução de regras e de árvores de decisão tem se mostrado uma… (more)

▼

Dentre as diversas tarefas em que os algoritmos evolutivos têm sido empregados, a indução de regras e de árvores de decisão tem se mostrado uma abordagem bastante atrativa em diversos domínios de aplicação. Algoritmos de indução de árvores de decisão representam uma das técnicas mais populares em problemas de classificação. Entretanto, os algoritmos tradicionais de indução apresentam algumas limitações, pois, geralmente, usam uma estratégia gulosa, top down e com particionamento recursivo para a construção das árvores. Esses fatores degradam a qualidade dos dados, os quais podem gerar regras estatisticamente não significativas. Este trabalho propõe o algoritmo LEGAL-Tree, uma nova abordagem baseada em algoritmos genéticos para indução de árvores de decisão. O algoritmo proposto visa evitar a estratégia gulosa e a convergência para ótimos locais. Para isso, esse algoritmo adota uma abordagem multi-objetiva lexicográfica. Nos experimentos realizados sobre bases de dados de diversos problemas de classificação, a função de fitness de LEGAL-Tree considera as duas medidas mais comuns para avaliação das árvores de decisão: acurácia e tamanho da árvore. Os resultados obtidos mostraram que LEGAL-Tree teve um desempenho equivalente ao algoritmo SimpleCart (implementação em Java do algoritmo CART) e superou o tradicional algoritmo J48 (implementação em Java do algoritmo C4.5), além de ter superado também o algoritmo evolutivo GALE. A principal contribuição de LEGAL-Tree não foi gerar árvores com maior acurácia preditiva, mas sim gerar árvores menores e, portanto, mais compreensíveis ao usuário do que as outras abordagens, mantendo a acurácia preditiva equivalente. Isso mostra que LEGAL-Tree obteve sucesso na otimização lexicográfica de seus objetivos, uma vez que a idéia era justamente dar preferência às árvores menores (em termos de número de nodos) quando houvesse equivalência de acurácia

Among the several tasks evolutionary algorithms have been successfully employed, the induction of classification rules and decision trees has been shown to be a relevant approach for several application domains. Decision tree induction algorithms represent one of the most popular techniques for dealing with classification problems. However, conventionally used decision trees induction algorithms present limitations due to the strategy they usually implement: recursive top-down data partitioning through a greedy split evaluation. The main problem with this strategy is quality loss during the partitioning process, which can lead to statistically insignificant rules. In this thesis we propose the LEGAL-Tree algorithm, a new GA-based algorithm for decision tree induction. The proposed algorithm aims to prevent the greedy strategy and to avoid converging to local optima. For such, it is based on a lexicographic multi-objective approach. In the experiments which were run in several classification problems, LEGAL-Tree\'s fitness function considers two of the most common measures to evaluate decision trees: accuracy and tree size. Results show…

Advisors/Committee Members: Carvalho, André Carlos Ponce de Leon Ferreira de, Freitas, Alex Alves.

Subjects/Keywords: Algoritmos genéticos; Árvores de decisão; Classificação; Classification; Decision trees; Genetic algoithms

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Basgalupp, M. P. (2010). LEGAL-Tree: um algoritmo genético multi-objetivo para indução de árvores de decisão. (Doctoral Dissertation). University of São Paulo. Retrieved from http://www.teses.usp.br/teses/disponiveis/55/55134/tde-12052010-165344/ ;

Chicago Manual of Style (16^th Edition):

Basgalupp, Márcio Porto. “LEGAL-Tree: um algoritmo genético multi-objetivo para indução de árvores de decisão.” 2010. Doctoral Dissertation, University of São Paulo. Accessed January 23, 2021. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-12052010-165344/ ;.

MLA Handbook (7^th Edition):

Basgalupp, Márcio Porto. “LEGAL-Tree: um algoritmo genético multi-objetivo para indução de árvores de decisão.” 2010. Web. 23 Jan 2021.

Vancouver:

Basgalupp MP. LEGAL-Tree: um algoritmo genético multi-objetivo para indução de árvores de decisão. [Internet] [Doctoral dissertation]. University of São Paulo; 2010. [cited 2021 Jan 23]. Available from: http://www.teses.usp.br/teses/disponiveis/55/55134/tde-12052010-165344/ ;.

Council of Science Editors:

Basgalupp MP. LEGAL-Tree: um algoritmo genético multi-objetivo para indução de árvores de decisão. [Doctoral Dissertation]. University of São Paulo; 2010. Available from: http://www.teses.usp.br/teses/disponiveis/55/55134/tde-12052010-165344/ ;

University of Alberta

21. Das Gupta, Ujjwal. Adaptive Representation for Policy Gradient.

Degree: MS, Department of Computing Science, 2015, University of Alberta

URL: https://era.library.ualberta.ca/files/zk51vk289

► Much of the focus on finding good representations in reinforcement learning has been on learning complex non-linear predictors of value. Methods like policy gradient, that… (more)

Subjects/Keywords: Representation Learning; Decision Trees; Policy Gradient; Reinforcement Learning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Das Gupta, U. (2015). Adaptive Representation for Policy Gradient. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/zk51vk289

Chicago Manual of Style (16^th Edition):

Das Gupta, Ujjwal. “Adaptive Representation for Policy Gradient.” 2015. Masters Thesis, University of Alberta. Accessed January 23, 2021. https://era.library.ualberta.ca/files/zk51vk289.

MLA Handbook (7^th Edition):

Das Gupta, Ujjwal. “Adaptive Representation for Policy Gradient.” 2015. Web. 23 Jan 2021.

Vancouver:

Das Gupta U. Adaptive Representation for Policy Gradient. [Internet] [Masters thesis]. University of Alberta; 2015. [cited 2021 Jan 23]. Available from: https://era.library.ualberta.ca/files/zk51vk289.

Council of Science Editors:

Das Gupta U. Adaptive Representation for Policy Gradient. [Masters Thesis]. University of Alberta; 2015. Available from: https://era.library.ualberta.ca/files/zk51vk289

22. Silveira, Josimara de Ávila. Análise de sinais cerebrais utilizando árvores de decisão.

Degree: 2013, Universidade Federal do Rio Grande

URL: http://repositorio.furg.br/handle/1/6409

►

Este trabalho propõe um estudo de sinais cerebrais aplicados em sistemas BCI (Brain-Computer Interface - Interfaces Cérebro Computador), através do uso de Árvores de Decisão… (more)

▼

Este trabalho propõe um estudo de sinais cerebrais aplicados em sistemas BCI (Brain-Computer Interface - Interfaces Cérebro Computador), através do uso de Árvores de Decisão e da análise dessas árvores com base nas Neurociências. Para realizar o tratamento dos dados são necessárias 5 fases: aquisição de dados, pré-processamento, extração de características, classificação e validação. Neste trabalho, todas as fases são contempladas. Contudo, enfatiza-se as fases de classificação e de validação. Na classificação utiliza-se a técnica de Inteligência Artificial denominada Árvores de Decisão. Essa técnica é reconhecida na literatura como uma das formas mais simples e bem sucedidas de algoritmos de aprendizagem. Já a fase de validação é realizada nos estudos baseados na Neurociência, que é um conjunto das disciplinas que estudam o sistema nervoso, sua estrutura, seu desenvolvimento, funcionamento, evolução, relação com o comportamento e a mente, e também suas alterações. Os resultados obtidos neste trabalho são promissores, mesmo sendo iniciais, visto que podem melhor explicar, com a utilização de uma forma automática, alguns processos cerebrais.

This work proposes a study on brain signals applied to BCI (Brain-Computer Interface) systems, through the use of Decision Trees and the analysis of these trees based on Neuroscience. To treat the data, this system must execute ve stages: data acquisition, preprocessing, feature extraction, classi cation and validation. In this work, all phases are executed. However, it emphasizes the classi cation and validation phases. In the classi cation, it uses the Arti cial Intelligence technique called Decision Trees. This technique is known in the literature as one of the most successful and simpler learning algorithms. The validation phase is based on studies performed in Neuroscience, which is a set of disciplines that study the nervous system, its structure, its development, operation, evolution, behavior and relationship with the mind, and also your changes. The results of this study are promising, even initials, since they can better explain, with the use of an automated way, some brain processes.

Advisors/Committee Members: Adamatti, Diana Francisca, Carvalho, Fernanda Antoniolo Hammes de.

Subjects/Keywords: Sistemas BCI; Árvores de decisão; Neurociência; BCI systems; Decision trees; Neuroscience

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Silveira, J. d. �. (2013). Análise de sinais cerebrais utilizando árvores de decisão. (Masters Thesis). Universidade Federal do Rio Grande. Retrieved from http://repositorio.furg.br/handle/1/6409

Chicago Manual of Style (16^th Edition):

Silveira, Josimara de Ávila. “Análise de sinais cerebrais utilizando árvores de decisão.” 2013. Masters Thesis, Universidade Federal do Rio Grande. Accessed January 23, 2021. http://repositorio.furg.br/handle/1/6409.

MLA Handbook (7^th Edition):

Silveira, Josimara de Ávila. “Análise de sinais cerebrais utilizando árvores de decisão.” 2013. Web. 23 Jan 2021.

Vancouver:

Silveira Jd�. Análise de sinais cerebrais utilizando árvores de decisão. [Internet] [Masters thesis]. Universidade Federal do Rio Grande; 2013. [cited 2021 Jan 23]. Available from: http://repositorio.furg.br/handle/1/6409.

Council of Science Editors:

Silveira Jd�. Análise de sinais cerebrais utilizando árvores de decisão. [Masters Thesis]. Universidade Federal do Rio Grande; 2013. Available from: http://repositorio.furg.br/handle/1/6409

Universidade do Rio Grande do Sul

23. Signor, Bruna. Qualidade técnica e reparo periapical em retratamentos endodônticos : estudo observacional.

Degree: 2017, Universidade do Rio Grande do Sul

URL: http://hdl.handle.net/10183/179746

► Introdução: Retratamentos endodônticos apresentam maior complexidade técnica e piores prognósticos quando comparados ao tratamento endodôntico inicial. Nesse contexto, uma investigação mais detalhada em relação aos… (more)

▼ Introdução: Retratamentos endodônticos apresentam maior complexidade técnica e piores prognósticos quando comparados ao tratamento endodôntico inicial. Nesse contexto, uma investigação mais detalhada em relação aos fatores que afetam a exiquibilidade de se obter qualidade técnica satisfatória e reparo periapical é necessária. Técnicas empregadas para mineração de dados são pouco exploradas na área da Odontologia, ainda que apresentem potencial em contribuir com a descoberta do conhecimento. No presente estudo, padrões e fatores de risco relacionados à qualidade técnica e ao reparo periapical de retratamentos endodônticos foram investigados. Árvores de decisão foram geradas, sendo essa técnica complementada pela análise estatística convencional. Metodologia: Este estudo observacional incluiu 321 indivíduos com indicação de retratamento endodôntico atendidos por alunos de especialização em Endodontia. Foram coletados dados demográficos, referentes a história médica, ao diagnóstico, ao tratamento e a controles pós-operatórios, os quais foram transferidos para uma base de dados eletrônica. Após o preparo e pré-processamento de dados, foram selecionadas 32 variáveis independentes e 2 variáveis dependentes, as quais compreenderam os desfechos qualidade técnica do retratamento e reparo periapical. Estatísticas descritivas foram conduzidas a fim de determinar a frequência de dados ausentes, a distribuição das variáveis categóricas e a média e desvio-padrão de variáveis numéricas. Foram geradas árvores de decisão para a determinação de padrões relacionados aos desfechos, através do software de mineração de dados Weka (Waikato Environment of Knowledge Analysis, University of Waikato, New Zealand). Análises estatísticas convencionais foram conduzidas com auxílio do Software SPSS (SPSS Inc., Chicago, IL, USA), a fim de determinar fatores que poderiam interferir nos referidos desfechos. Resultados: Após o retratamento endodôntico, qualidade técnica satisfatória e reparo periapical foram obtidos em 65,20% e em 80,50% dos casos, respectivamente. A qualidade técnica do retratamento endodôntico foi afetada por vários fatores de risco, incluindo curvatura radicular severa (p < 0,001) e alterações na morfologia do canal radicular (p = 0,002). As árvores de decisão sugeriram padrões que combinam a ocorrência simultânea de raízes retas e reabsorções radiculares apicais com resultados tecnicamente insatisfatórios. O diâmetro da lesão periapical (p = 0,018), o grupo dentário (p = 0,015) e a presença de reabsorções apicais (p = 0,024) apresentaram associação significativa com o insucesso de retratamentos endodônticos. A análise de mineração de dados sugeriu que lesões periapicais extensas e qualidade da obturação insatisfatória no tratamento endodôntico inicial, apresentam mecanismos de interação entre a infecção intracanal e a resposta do hospedeiro que não foram totalmente elucidados, sendo necessários estudos complementares. Conclusão: Qualidade técnica satisfatória é afetada por diversos fatores de risco, entre eles, a presença de… Advisors/Committee Members: Scarparo, Roberta Kochenborger.

Subjects/Keywords: Retreatment; Endodontia; Retratamento endodôntico; Endodontics; Decision trees; Data mining

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Signor, B. (2017). Qualidade técnica e reparo periapical em retratamentos endodônticos : estudo observacional. (Thesis). Universidade do Rio Grande do Sul. Retrieved from http://hdl.handle.net/10183/179746

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Signor, Bruna. “Qualidade técnica e reparo periapical em retratamentos endodônticos : estudo observacional.” 2017. Thesis, Universidade do Rio Grande do Sul. Accessed January 23, 2021. http://hdl.handle.net/10183/179746.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Signor, Bruna. “Qualidade técnica e reparo periapical em retratamentos endodônticos : estudo observacional.” 2017. Web. 23 Jan 2021.

Vancouver:

Signor B. Qualidade técnica e reparo periapical em retratamentos endodônticos : estudo observacional. [Internet] [Thesis]. Universidade do Rio Grande do Sul; 2017. [cited 2021 Jan 23]. Available from: http://hdl.handle.net/10183/179746.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Signor B. Qualidade técnica e reparo periapical em retratamentos endodônticos : estudo observacional. [Thesis]. Universidade do Rio Grande do Sul; 2017. Available from: http://hdl.handle.net/10183/179746

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

King Abdullah University of Science and Technology

24. Busbait, Monther I. Diagnosis of Constant Faults in Read-Once Contact Networks over Finite Bases using Decision Trees.

Degree: 2014, King Abdullah University of Science and Technology

URL: http://hdl.handle.net/10754/316551

► We study the depth of decision trees for diagnosis of constant faults in read-once contact networks over finite bases. This includes diagnosis of 0-1 faults,… (more)

Subjects/Keywords: read-once contact networks; constant faults; decision trees

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Busbait, M. I. (2014). Diagnosis of Constant Faults in Read-Once Contact Networks over Finite Bases using Decision Trees. (Thesis). King Abdullah University of Science and Technology. Retrieved from http://hdl.handle.net/10754/316551

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Busbait, Monther I. “Diagnosis of Constant Faults in Read-Once Contact Networks over Finite Bases using Decision Trees.” 2014. Thesis, King Abdullah University of Science and Technology. Accessed January 23, 2021. http://hdl.handle.net/10754/316551.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Busbait, Monther I. “Diagnosis of Constant Faults in Read-Once Contact Networks over Finite Bases using Decision Trees.” 2014. Web. 23 Jan 2021.

Vancouver:

Busbait MI. Diagnosis of Constant Faults in Read-Once Contact Networks over Finite Bases using Decision Trees. [Internet] [Thesis]. King Abdullah University of Science and Technology; 2014. [cited 2021 Jan 23]. Available from: http://hdl.handle.net/10754/316551.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Busbait MI. Diagnosis of Constant Faults in Read-Once Contact Networks over Finite Bases using Decision Trees. [Thesis]. King Abdullah University of Science and Technology; 2014. Available from: http://hdl.handle.net/10754/316551

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Miami

25. Sendur, Zeynel. Text Document Categorization by Machine Learning.

Degree: MS, Electrical and Computer Engineering (Engineering), 2008, University of Miami

URL: https://scholarlyrepository.miami.edu/oa_theses/209

► Because of the explosion of digital and online text information, automatic organization of documents has become a very important research area. There are mainly… (more)

Subjects/Keywords: Decision Trees; Multi-label Classification

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Sendur, Z. (2008). Text Document Categorization by Machine Learning. (Thesis). University of Miami. Retrieved from https://scholarlyrepository.miami.edu/oa_theses/209

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Sendur, Zeynel. “Text Document Categorization by Machine Learning.” 2008. Thesis, University of Miami. Accessed January 23, 2021. https://scholarlyrepository.miami.edu/oa_theses/209.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Sendur, Zeynel. “Text Document Categorization by Machine Learning.” 2008. Web. 23 Jan 2021.

Vancouver:

Sendur Z. Text Document Categorization by Machine Learning. [Internet] [Thesis]. University of Miami; 2008. [cited 2021 Jan 23]. Available from: https://scholarlyrepository.miami.edu/oa_theses/209.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Sendur Z. Text Document Categorization by Machine Learning. [Thesis]. University of Miami; 2008. Available from: https://scholarlyrepository.miami.edu/oa_theses/209

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Tampere University

26. Pojon, Murat. Using Machine Learning to Predict Student Performance .

Degree: 2017, Tampere University

URL: https://trepo.tuni.fi/handle/10024/101646

► This thesis examines the application of machine learning algorithms to predict whether a student will be successful or not. The specific focus of the thesis… (more)

Subjects/Keywords: student performance; machine learning; regression; naïve Bayes classification; decision trees

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Pojon, M. (2017). Using Machine Learning to Predict Student Performance . (Masters Thesis). Tampere University. Retrieved from https://trepo.tuni.fi/handle/10024/101646

Chicago Manual of Style (16^th Edition):

Pojon, Murat. “Using Machine Learning to Predict Student Performance .” 2017. Masters Thesis, Tampere University. Accessed January 23, 2021. https://trepo.tuni.fi/handle/10024/101646.

MLA Handbook (7^th Edition):

Pojon, Murat. “Using Machine Learning to Predict Student Performance .” 2017. Web. 23 Jan 2021.

Vancouver:

Pojon M. Using Machine Learning to Predict Student Performance . [Internet] [Masters thesis]. Tampere University; 2017. [cited 2021 Jan 23]. Available from: https://trepo.tuni.fi/handle/10024/101646.

Council of Science Editors:

Pojon M. Using Machine Learning to Predict Student Performance . [Masters Thesis]. Tampere University; 2017. Available from: https://trepo.tuni.fi/handle/10024/101646

University of Houston

27. Amalaman, Paul K. 1966-. New Approaches to Hierarchical Modeling — Frameworks, Algorithms, and Applications.

Degree: PhD, Computer Science, 2015, University of Houston

URL: http://hdl.handle.net/10657/4888

► Obtaining hierarchical organizations of knowledge is important in many domains. To create such hierarchies, improved techniques for subdividing entities hierarchically ac-cording to similarities and differences… (more)

▼ Obtaining hierarchical organizations of knowledge is important in many domains. To create such hierarchies, improved techniques for subdividing entities hierarchically ac-cording to similarities and differences are needed. New techniques for organizing docu-ments in hierarchies, for automatic document retrieval and for hierarchical query cluster-ing are being made available at a fast pace. In this work, we investigate new methods to induce hierarchical models with the goal of obtaining better predictive models, to facili-tate the creation of background knowledge with respect to an underlining class distribu-tion, to obtain hierarchical groupings of a set of objects based on background knowledge they share, to detect sub-classes within existing class distribution, and to provide methods to evaluate hierarchical groupings. The results of this effort has led to the development of (1) TPRTI, a new regression tree induction approach which uses turning points, candi-dates split points computed before the recursive process takes place, to recursively split the node datasets; (2) PATHFINDER, a new classification tree induction capable of in-ducing very short trees with high accuracies for the price of not classifying examples deemed difficult to classify; (3) AVALANCHE, a new hierarchical divisive clustering approach which takes as input a distance matrix and forms clusters maximizing inter-cluster distances; (4) STAXAC, a new agglomerative clustering approach which creates supervised taxonomies that unlike traditional agglomerative clustering, which only uses proximity as the single criterion for merging, uses both proximity and class labels infor-mation to obtain hierarchical groupings of a set of objects. We applied the techniques we developed, (1) to molecular phylogenetic-based taxonomy generation and found that this new approach and the obtained supervised taxonomies can help biologists better charac-terize organisms according to some characteristics of interest such as diseases, growth rate, etc.; (2) to data editing; we were able to enhance the accuracy of the k-nearest neighbor classifier by removing minority class examples from clusters that were extracted from a supervised taxonomy; (3) to meta learning; we developed new algorithms that operate on supervised taxonomies and compute both the distribution of the classes within a dataset, and the difficulty of classifying examples belonging to a particular dataset. Advisors/Committee Members: Eick, Christoph F. (advisor), Vilalta, Ricardo (committee member), Shi, Weidong (committee member), Shah, Shishir Kirit (committee member), Cooper, Timothy F. (committee member).

Subjects/Keywords: Decision trees; Regression tree; Classification tree; Supervised taxonomy; Hierarchical clustering

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Amalaman, P. K. 1. (2015). New Approaches to Hierarchical Modeling — Frameworks, Algorithms, and Applications. (Doctoral Dissertation). University of Houston. Retrieved from http://hdl.handle.net/10657/4888

Chicago Manual of Style (16^th Edition):

Amalaman, Paul K 1966-. “New Approaches to Hierarchical Modeling — Frameworks, Algorithms, and Applications.” 2015. Doctoral Dissertation, University of Houston. Accessed January 23, 2021. http://hdl.handle.net/10657/4888.

MLA Handbook (7^th Edition):

Amalaman, Paul K 1966-. “New Approaches to Hierarchical Modeling — Frameworks, Algorithms, and Applications.” 2015. Web. 23 Jan 2021.

Vancouver:

Amalaman PK1. New Approaches to Hierarchical Modeling — Frameworks, Algorithms, and Applications. [Internet] [Doctoral dissertation]. University of Houston; 2015. [cited 2021 Jan 23]. Available from: http://hdl.handle.net/10657/4888.

Council of Science Editors:

Amalaman PK1. New Approaches to Hierarchical Modeling — Frameworks, Algorithms, and Applications. [Doctoral Dissertation]. University of Houston; 2015. Available from: http://hdl.handle.net/10657/4888

Central Connecticut State University

28. Allard, Jeffrey Michael, 1973-. An Application of Gradient Boosted Regression Trees and Random Forests to Prospect Direct Marketing Response Modeling.

Degree: Department of Mathematical Sciences, 2013, Central Connecticut State University

URL: http://content.library.ccsu.edu/u?/ccsutheses,1922

►

A business that conducts direct marketing activities (e.g. direct mail, outbound-telemarketing, email marketing) has two primary concerns regarding the management of its customer base –… (more)

▼

A business that conducts direct marketing activities (e.g. direct mail, outbound-telemarketing, email marketing) has two primary concerns regarding the management of its customer base – (1) maintaining and deepening the relationship value with current / past customers and (2) acquisition of new customers. The use of predictive models specifically and data science generally, applied to maintaining current customer relationships is well known and is composed of related activities such as cross-sell (finding the "next best offer"), retention (intervening prior to customer attrition) and measurement of and experimentation to increase lifetime value (LTV). The richness of data captured on current customers as they interact with a business allows for continuous analysis and optimization of efforts aimed at these outcomes. For example, order/transaction history, web site visits, customer service interaction, social media usage, collected demographics and past responsiveness to marketing can be leveraged for data mining. Acquisition of new customers, in a targeted sense (i.e. not mass advertising) is more difficult, more expensive and less well studied. The fact that a prospect, by definition, has had no past (purchase) interaction with a brand greatly limits the amount of data that can be analyzed. Direct marketing to prospects requires the ability to identify prospects as individual units (consumers, businesses or households), acquire the means to contact them (e.g. street address, telephone number, email address) and most importantly, decide which prospects to contact based on expected return on investment. Co-operative databases exist as a means to collect and aggregate data for prospecting. These databases become a surrogate for the company's own customer database and are a powerful means from which to identify, locate and select the most likely to respond prospect units. This thesis examines a specific example of mining a business-to-business (B2B) co-operative database for the purpose of selecting prospect units to which to market with a direct mail catalog. Specifically, we show the efficacy of using modern machine learning algorithms, Gradient Boosted Regression Trees and Random Forests, to build highly predictive models to forecast response of prospect units to direct mail marketing offers. Within this thesis, we examine the algorithms in detail, apply them to a real life problem and use the models to further explain the underlying processes associated with the identification of those most likely to respond to a marketing offer.

"Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science in Data Mining."; Thesis advisor: Darius Dziuda.; M.S.,Central Connecticut State University,,2013.;

Advisors/Committee Members: Dziuda, Darius M..

Subjects/Keywords: Direct marketing.; Database marketing.; Decision trees.; Data mining.

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APA (6^th Edition):

Allard, Jeffrey Michael, 1. (2013). An Application of Gradient Boosted Regression Trees and Random Forests to Prospect Direct Marketing Response Modeling. (Thesis). Central Connecticut State University. Retrieved from http://content.library.ccsu.edu/u?/ccsutheses,1922

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Allard, Jeffrey Michael, 1973-. “An Application of Gradient Boosted Regression Trees and Random Forests to Prospect Direct Marketing Response Modeling.” 2013. Thesis, Central Connecticut State University. Accessed January 23, 2021. http://content.library.ccsu.edu/u?/ccsutheses,1922.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Allard, Jeffrey Michael, 1973-. “An Application of Gradient Boosted Regression Trees and Random Forests to Prospect Direct Marketing Response Modeling.” 2013. Web. 23 Jan 2021.

Vancouver:

Allard, Jeffrey Michael 1. An Application of Gradient Boosted Regression Trees and Random Forests to Prospect Direct Marketing Response Modeling. [Internet] [Thesis]. Central Connecticut State University; 2013. [cited 2021 Jan 23]. Available from: http://content.library.ccsu.edu/u?/ccsutheses,1922.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Allard, Jeffrey Michael 1. An Application of Gradient Boosted Regression Trees and Random Forests to Prospect Direct Marketing Response Modeling. [Thesis]. Central Connecticut State University; 2013. Available from: http://content.library.ccsu.edu/u?/ccsutheses,1922

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Université Catholique de Louvain

29. Amouh, Teh. Analysis of tabular non-standard data with decision trees, and application to hypnogram-based detection of sleep profile.

Degree: 2011, Université Catholique de Louvain

URL: http://hdl.handle.net/2078.1/105005

► The amount of data in the world and in our lives seems ever-increasing and there is no end in sight. Such a situation is supported… (more)

▼ The amount of data in the world and in our lives seems ever-increasing and there is no end in sight. Such a situation is supported by omnipresent computers along with inexpensive disks and online storage. In order to get the best from these data that overwhelm us, computers give us the opportunity to analyse them for decision making. For example, as reported in the literature, dairy farmers in New Zealand have to make a tough business decision every year : which cows to retain in their herd and which to sell off to an abattoir. Each cow's breeding and milk production history, age, health problems, and and many other factors influence this decision. About 700 attributes for each of several million cows have been recorded over the years. This is an example of large data set (large number of individuals : several millions) containing high-dimensional descriptions (large number of variables : 700). Classically, the observed value on each variable for each individual has a scalar data type. Large multidimensional data sets abound in real applications. Large size and high dimension are two aspects of the complexity inherent in these data sets. In the framework of this thesis, we are not interested in the complexity induced by the dimensionality... One way to deal with large size data sets is to summarize the data and use adequate methods for mining the summarized data. Summarising data, as we understand it here, does not reduce the dimensionality. It reduces the number of individuals. In the summarized data table, each row can be viewed as the description of a concept, which is a high level individual (for example a given species of birds) containing lower level individuals in its extent (for instance all birds from the given species). The variability of elements in an extent should be revealed by the row describing the concept in the summarized table, hence the use of data structures in the cells of the summarized data table. Summarising data using data structures leads to descriptors which are no longer scalar type attributes. For instance, knowing that a cow nears the end of its productive life at 8 years, one might group together all cows which are at least 8 years old. For such a group, the observed value on attribute 'AGE' would be an interval like [8,15], if we assume that there is no cow over 15 years old. Likewise, the other attributes used to describe a group of cows would be structure-valued, leading to a non-standard data table. In other words, each value of a variable used to describe a group of cows would be a structure. Intervals, multivalued-data, distributions, histograms, functions, time series, graphs, and so on are examples of structures. To our knowledge, there is currently only one complete software environment that is publicly and freely available for tabular structure-valued data analysis. The Symbolic Objects Data Analysis System (SODAS2) was designed and implemented as a "black-box" and consequently does not provide necessary flexibility and adaptability in order to support research activity… Advisors/Committee Members: UCL - SST/ICTM/ELEN - Pôle en ingénierie électrique, Macq, Benoît, Noirhomme-Fraiture, Monique, De Vleeschouwer, Christophe, Batagelj, Vladimir, Gosselin , Bernard, Lechevallier, Yves, Verleysen, Michel.

Subjects/Keywords: Supervised classification; Clustering; Decision trees; Non-standard data; Sleep analysis

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Amouh, T. (2011). Analysis of tabular non-standard data with decision trees, and application to hypnogram-based detection of sleep profile. (Thesis). Université Catholique de Louvain. Retrieved from http://hdl.handle.net/2078.1/105005

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Amouh, Teh. “Analysis of tabular non-standard data with decision trees, and application to hypnogram-based detection of sleep profile.” 2011. Thesis, Université Catholique de Louvain. Accessed January 23, 2021. http://hdl.handle.net/2078.1/105005.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Amouh, Teh. “Analysis of tabular non-standard data with decision trees, and application to hypnogram-based detection of sleep profile.” 2011. Web. 23 Jan 2021.

Vancouver:

Amouh T. Analysis of tabular non-standard data with decision trees, and application to hypnogram-based detection of sleep profile. [Internet] [Thesis]. Université Catholique de Louvain; 2011. [cited 2021 Jan 23]. Available from: http://hdl.handle.net/2078.1/105005.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Amouh T. Analysis of tabular non-standard data with decision trees, and application to hypnogram-based detection of sleep profile. [Thesis]. Université Catholique de Louvain; 2011. Available from: http://hdl.handle.net/2078.1/105005

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Durham University

30. Summad, Emad. A Monte-Carlo approach to tool selection for sheet metal punching and nibbling.

Degree: PhD, 2001, Durham University

URL: http://etheses.dur.ac.uk/4137/ ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.342613

► Selecting the best set of tools to produce certain geometrical shapes/features in sheet metal punching is one of the problems that has a great effect… (more)

▼ Selecting the best set of tools to produce certain geometrical shapes/features in sheet metal punching is one of the problems that has a great effect on product development time, cost and achieved quality. The trend nowadays is, where at all possible, to limit design to the use of standard tools. Such an option makes the problem of selecting the appropriate set of tools even more complex, especially when considering that sheet metal features can have a wide range of complex shapes. Another dimension of complexity is limited tool rack capacity. Thus, an inappropriate tool selection strategy will lead to punching inefficiency and may require frequent stopping of the machine and replacing the required tools, which is a rather expensive and time consuming exercise. This work demonstrates that the problem of selecting the best set of tools is actually a process of searching an explosive decision tree. The difficulty in searching such types of decision trees is that intermediate decisions do not necessarily reflect the total cost implication of carrying out such a decision. A new approach to solve such a complex optimisation problem using the Monte Carlo Simulation Methods has been introduced in this thesis. The aim of the present work was to establish the use of Monte Carlo methods as an "assumptions or rule free" baseline or benchmark for the assessment of search strategies. A number of case studies are given, where the feasibility of Monte Carlo Simulation Methods as an efficient and viable method to optimise such a complex optimisation problem is demonstrated. The use of a Monte Carlo approach for selecting the best set of punching tools, showed an interesting point, that is, the effect of dominant "one-to-one" feature/tool matches on the efficiency of the search. This naturally led on to the need of a search methodology that will be more efficient than the application of the Monte Carlo method alone. This thesis presents some interesting speculations for a hybrid approach to tool selection to achieve a better solution than the use of the Monte Carlo method alone to achieve the optimum solution in a shorter time.

Subjects/Keywords: 670; Decision trees; Simulation; Tools

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Summad, E. (2001). A Monte-Carlo approach to tool selection for sheet metal punching and nibbling. (Doctoral Dissertation). Durham University. Retrieved from http://etheses.dur.ac.uk/4137/ ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.342613

Chicago Manual of Style (16^th Edition):

Summad, Emad. “A Monte-Carlo approach to tool selection for sheet metal punching and nibbling.” 2001. Doctoral Dissertation, Durham University. Accessed January 23, 2021. http://etheses.dur.ac.uk/4137/ ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.342613.

MLA Handbook (7^th Edition):

Summad, Emad. “A Monte-Carlo approach to tool selection for sheet metal punching and nibbling.” 2001. Web. 23 Jan 2021.

Vancouver:

Summad E. A Monte-Carlo approach to tool selection for sheet metal punching and nibbling. [Internet] [Doctoral dissertation]. Durham University; 2001. [cited 2021 Jan 23]. Available from: http://etheses.dur.ac.uk/4137/ ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.342613.

Council of Science Editors:

Summad E. A Monte-Carlo approach to tool selection for sheet metal punching and nibbling. [Doctoral Dissertation]. Durham University; 2001. Available from: http://etheses.dur.ac.uk/4137/ ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.342613

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Open Access Theses and Dissertations