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You searched for subject:( en FEATURE SELECTION). Showing records 1 – 30 of 46243 total matches.

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University of Manchester

1. Nogueira, Sarah. Quantifying the Stability of Feature Selection.

Degree: 2018, University of Manchester

Feature Selection is central to modern data science, from exploratory data analysis to predictive model-building. The "stability"of a feature selection algorithm refers to the robustness… (more)

Subjects/Keywords: Stability; Feature Selection; Variable Selection

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

APA (6th Edition):

Nogueira, S. (2018). Quantifying the Stability of Feature Selection. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:313287

Chicago Manual of Style (16th Edition):

Nogueira, Sarah. “Quantifying the Stability of Feature Selection.” 2018. Doctoral Dissertation, University of Manchester. Accessed October 30, 2020. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:313287.

MLA Handbook (7th Edition):

Nogueira, Sarah. “Quantifying the Stability of Feature Selection.” 2018. Web. 30 Oct 2020.

Vancouver:

Nogueira S. Quantifying the Stability of Feature Selection. [Internet] [Doctoral dissertation]. University of Manchester; 2018. [cited 2020 Oct 30]. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:313287.

Council of Science Editors:

Nogueira S. Quantifying the Stability of Feature Selection. [Doctoral Dissertation]. University of Manchester; 2018. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:313287


Delft University of Technology

2. Van Schooten, S. (author); Harel, R. (author); Ercan, S. (author). Applying feature selection methods on fMRI data.

Degree: 2014, Delft University of Technology

In neuroscience, the ability to correlate and classify certain activity patterns of the brain to different physical and mental states of the subject is of… (more)

Subjects/Keywords: fMRI; feature selection

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APA (6th Edition):

Van Schooten, S. (author); Harel, R. (author); Ercan, S. (. (2014). Applying feature selection methods on fMRI data. (Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:24680427-ae9e-4ddc-8e6a-1689a00a1cc9

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

Chicago Manual of Style (16th Edition):

Van Schooten, S. (author); Harel, R. (author); Ercan, S (author). “Applying feature selection methods on fMRI data.” 2014. Thesis, Delft University of Technology. Accessed October 30, 2020. http://resolver.tudelft.nl/uuid:24680427-ae9e-4ddc-8e6a-1689a00a1cc9.

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

MLA Handbook (7th Edition):

Van Schooten, S. (author); Harel, R. (author); Ercan, S (author). “Applying feature selection methods on fMRI data.” 2014. Web. 30 Oct 2020.

Vancouver:

Van Schooten, S. (author); Harel, R. (author); Ercan S(. Applying feature selection methods on fMRI data. [Internet] [Thesis]. Delft University of Technology; 2014. [cited 2020 Oct 30]. Available from: http://resolver.tudelft.nl/uuid:24680427-ae9e-4ddc-8e6a-1689a00a1cc9.

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

Council of Science Editors:

Van Schooten, S. (author); Harel, R. (author); Ercan S(. Applying feature selection methods on fMRI data. [Thesis]. Delft University of Technology; 2014. Available from: http://resolver.tudelft.nl/uuid:24680427-ae9e-4ddc-8e6a-1689a00a1cc9

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


Pontifical Catholic University of Rio de Janeiro

3. PAULA DE CASTRO SONNENFELD VILELA. [pt] CLASSIFICAÇÃO DE SENTIMENTO PARA NOTÍCIAS SOBRE A PETROBRAS NO MERCADO FINANCEIRO.

Degree: 2011, Pontifical Catholic University of Rio de Janeiro

[pt] Hoje em dia, encontramos uma grande quantidade de informações na internet, em particular, notícias sobre o mercado financeiro. Diversas pesquisas mostram que notícias sobre… (more)

Subjects/Keywords: [pt] APRENDIZADO DE MAQUINA; [en] MACHINE LEARNING; [pt] SELECAO DE ATRIBUTOS; [en] FEATURE SELECTION; [pt] CLASSIFICACAO DE TEXTOS; [en] TEXT CLASSIFICATION

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APA (6th Edition):

VILELA, P. D. C. S. (2011). [pt] CLASSIFICAÇÃO DE SENTIMENTO PARA NOTÍCIAS SOBRE A PETROBRAS NO MERCADO FINANCEIRO. (Thesis). Pontifical Catholic University of Rio de Janeiro. Retrieved from http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=18823

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

Chicago Manual of Style (16th Edition):

VILELA, PAULA DE CASTRO SONNENFELD. “[pt] CLASSIFICAÇÃO DE SENTIMENTO PARA NOTÍCIAS SOBRE A PETROBRAS NO MERCADO FINANCEIRO.” 2011. Thesis, Pontifical Catholic University of Rio de Janeiro. Accessed October 30, 2020. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=18823.

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

MLA Handbook (7th Edition):

VILELA, PAULA DE CASTRO SONNENFELD. “[pt] CLASSIFICAÇÃO DE SENTIMENTO PARA NOTÍCIAS SOBRE A PETROBRAS NO MERCADO FINANCEIRO.” 2011. Web. 30 Oct 2020.

Vancouver:

VILELA PDCS. [pt] CLASSIFICAÇÃO DE SENTIMENTO PARA NOTÍCIAS SOBRE A PETROBRAS NO MERCADO FINANCEIRO. [Internet] [Thesis]. Pontifical Catholic University of Rio de Janeiro; 2011. [cited 2020 Oct 30]. Available from: http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=18823.

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

Council of Science Editors:

VILELA PDCS. [pt] CLASSIFICAÇÃO DE SENTIMENTO PARA NOTÍCIAS SOBRE A PETROBRAS NO MERCADO FINANCEIRO. [Thesis]. Pontifical Catholic University of Rio de Janeiro; 2011. Available from: http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=18823

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


University of Technology, Sydney

4. Ubaudi, FA. Assessing a feature's trustworthiness and two approaches to feature selection.

Degree: 2011, University of Technology, Sydney

 Improvements in technology have led to a relentless deluge of information that current data mining approaches have trouble dealing with. An extreme example of this… (more)

Subjects/Keywords: Data mining.; Feature selection.; Feature trustworthiness.

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

APA (6th Edition):

Ubaudi, F. (2011). Assessing a feature's trustworthiness and two approaches to feature selection. (Thesis). University of Technology, Sydney. Retrieved from http://hdl.handle.net/10453/23392

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

Chicago Manual of Style (16th Edition):

Ubaudi, FA. “Assessing a feature's trustworthiness and two approaches to feature selection.” 2011. Thesis, University of Technology, Sydney. Accessed October 30, 2020. http://hdl.handle.net/10453/23392.

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

MLA Handbook (7th Edition):

Ubaudi, FA. “Assessing a feature's trustworthiness and two approaches to feature selection.” 2011. Web. 30 Oct 2020.

Vancouver:

Ubaudi F. Assessing a feature's trustworthiness and two approaches to feature selection. [Internet] [Thesis]. University of Technology, Sydney; 2011. [cited 2020 Oct 30]. Available from: http://hdl.handle.net/10453/23392.

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

Council of Science Editors:

Ubaudi F. Assessing a feature's trustworthiness and two approaches to feature selection. [Thesis]. University of Technology, Sydney; 2011. Available from: http://hdl.handle.net/10453/23392

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

5. Raheel, Saeed. L’Apprentissage artificiel pour la fouille de données multilingues : application à la classification automatique des documents arabes : Machine learning and the data mining of multilingual documents : application to the automatic classification of arabic documents.

Degree: Docteur es, Sciences de l'information et de la communication, 2010, Université Lumière – Lyon II

 La classification automatique des documents, une approche issue de l’apprentissage artificiel et de la fouille de textes, s’avère être très efficace pour l’organisation des ressources… (more)

Subjects/Keywords: Sélection d’attributs; Feature Selection

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APA (6th Edition):

Raheel, S. (2010). L’Apprentissage artificiel pour la fouille de données multilingues : application à la classification automatique des documents arabes : Machine learning and the data mining of multilingual documents : application to the automatic classification of arabic documents. (Doctoral Dissertation). Université Lumière – Lyon II. Retrieved from http://www.theses.fr/2010LYO20118

Chicago Manual of Style (16th Edition):

Raheel, Saeed. “L’Apprentissage artificiel pour la fouille de données multilingues : application à la classification automatique des documents arabes : Machine learning and the data mining of multilingual documents : application to the automatic classification of arabic documents.” 2010. Doctoral Dissertation, Université Lumière – Lyon II. Accessed October 30, 2020. http://www.theses.fr/2010LYO20118.

MLA Handbook (7th Edition):

Raheel, Saeed. “L’Apprentissage artificiel pour la fouille de données multilingues : application à la classification automatique des documents arabes : Machine learning and the data mining of multilingual documents : application to the automatic classification of arabic documents.” 2010. Web. 30 Oct 2020.

Vancouver:

Raheel S. L’Apprentissage artificiel pour la fouille de données multilingues : application à la classification automatique des documents arabes : Machine learning and the data mining of multilingual documents : application to the automatic classification of arabic documents. [Internet] [Doctoral dissertation]. Université Lumière – Lyon II; 2010. [cited 2020 Oct 30]. Available from: http://www.theses.fr/2010LYO20118.

Council of Science Editors:

Raheel S. L’Apprentissage artificiel pour la fouille de données multilingues : application à la classification automatique des documents arabes : Machine learning and the data mining of multilingual documents : application to the automatic classification of arabic documents. [Doctoral Dissertation]. Université Lumière – Lyon II; 2010. Available from: http://www.theses.fr/2010LYO20118


Université Catholique de Louvain

6. Gualberto Ferreira Coelho, Frederico. Semi-supervised feature selection.

Degree: 2013, Université Catholique de Louvain

As data acquisition has become relatively easy and inexpensive, data sets are becoming extremely large, both in the number of variables and in the number… (more)

Subjects/Keywords: Machine learning; Feature selection; Classification

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APA (6th Edition):

Gualberto Ferreira Coelho, F. (2013). Semi-supervised feature selection. (Thesis). Université Catholique de Louvain. Retrieved from http://hdl.handle.net/2078.1/128255

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

Chicago Manual of Style (16th Edition):

Gualberto Ferreira Coelho, Frederico. “Semi-supervised feature selection.” 2013. Thesis, Université Catholique de Louvain. Accessed October 30, 2020. http://hdl.handle.net/2078.1/128255.

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

MLA Handbook (7th Edition):

Gualberto Ferreira Coelho, Frederico. “Semi-supervised feature selection.” 2013. Web. 30 Oct 2020.

Vancouver:

Gualberto Ferreira Coelho F. Semi-supervised feature selection. [Internet] [Thesis]. Université Catholique de Louvain; 2013. [cited 2020 Oct 30]. Available from: http://hdl.handle.net/2078.1/128255.

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

Council of Science Editors:

Gualberto Ferreira Coelho F. Semi-supervised feature selection. [Thesis]. Université Catholique de Louvain; 2013. Available from: http://hdl.handle.net/2078.1/128255

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


University of Toronto

7. Komeili, Majid. Feature Selection, Cross-view Matching and Spoof Detection for Electrocardiogram-based Human Recognition.

Degree: PhD, 2017, University of Toronto

Biometric technology for identifying individuals has been extensively used all over the world. As this technology becomes increasingly popular, the potential of fooling or spoofing… (more)

Subjects/Keywords: Feature Selection; Medical Biometrics; 0984

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APA (6th Edition):

Komeili, M. (2017). Feature Selection, Cross-view Matching and Spoof Detection for Electrocardiogram-based Human Recognition. (Doctoral Dissertation). University of Toronto. Retrieved from http://hdl.handle.net/1807/98800

Chicago Manual of Style (16th Edition):

Komeili, Majid. “Feature Selection, Cross-view Matching and Spoof Detection for Electrocardiogram-based Human Recognition.” 2017. Doctoral Dissertation, University of Toronto. Accessed October 30, 2020. http://hdl.handle.net/1807/98800.

MLA Handbook (7th Edition):

Komeili, Majid. “Feature Selection, Cross-view Matching and Spoof Detection for Electrocardiogram-based Human Recognition.” 2017. Web. 30 Oct 2020.

Vancouver:

Komeili M. Feature Selection, Cross-view Matching and Spoof Detection for Electrocardiogram-based Human Recognition. [Internet] [Doctoral dissertation]. University of Toronto; 2017. [cited 2020 Oct 30]. Available from: http://hdl.handle.net/1807/98800.

Council of Science Editors:

Komeili M. Feature Selection, Cross-view Matching and Spoof Detection for Electrocardiogram-based Human Recognition. [Doctoral Dissertation]. University of Toronto; 2017. Available from: http://hdl.handle.net/1807/98800


UCLA

8. Chang, Kung-Hua. Complementarity In Data Mining.

Degree: Computer Science, 2015, UCLA

 A learning problem involving classifiers and features usually has three components: representation, evaluation, and optimization. Contemporary research represents classifiers and features as initially given, and… (more)

Subjects/Keywords: Computer science; Ensemble Selection; Feature Selection

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APA (6th Edition):

Chang, K. (2015). Complementarity In Data Mining. (Thesis). UCLA. Retrieved from http://www.escholarship.org/uc/item/8zn4s7mj

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

Chicago Manual of Style (16th Edition):

Chang, Kung-Hua. “Complementarity In Data Mining.” 2015. Thesis, UCLA. Accessed October 30, 2020. http://www.escholarship.org/uc/item/8zn4s7mj.

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

MLA Handbook (7th Edition):

Chang, Kung-Hua. “Complementarity In Data Mining.” 2015. Web. 30 Oct 2020.

Vancouver:

Chang K. Complementarity In Data Mining. [Internet] [Thesis]. UCLA; 2015. [cited 2020 Oct 30]. Available from: http://www.escholarship.org/uc/item/8zn4s7mj.

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

Council of Science Editors:

Chang K. Complementarity In Data Mining. [Thesis]. UCLA; 2015. Available from: http://www.escholarship.org/uc/item/8zn4s7mj

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


Addis Ababa University

9. ZEWDIE, MOSSIE. OPTIMAL FEATURE SELECTION FOR NETWORK INTRUSION DETECTION: A DATA MINING APPROACH .

Degree: 2012, Addis Ababa University

 The traditional approach in securing computer systems against cyber threats is designing mechanisms such as firewalls, authentication tools, and virtual private networks that create a… (more)

Subjects/Keywords: Cost sensitive feature selection; Cost insensitive feature selection; IGR; CFS.

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APA (6th Edition):

ZEWDIE, M. (2012). OPTIMAL FEATURE SELECTION FOR NETWORK INTRUSION DETECTION: A DATA MINING APPROACH . (Thesis). Addis Ababa University. Retrieved from http://etd.aau.edu.et/dspace/handle/123456789/2836

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

Chicago Manual of Style (16th Edition):

ZEWDIE, MOSSIE. “OPTIMAL FEATURE SELECTION FOR NETWORK INTRUSION DETECTION: A DATA MINING APPROACH .” 2012. Thesis, Addis Ababa University. Accessed October 30, 2020. http://etd.aau.edu.et/dspace/handle/123456789/2836.

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

MLA Handbook (7th Edition):

ZEWDIE, MOSSIE. “OPTIMAL FEATURE SELECTION FOR NETWORK INTRUSION DETECTION: A DATA MINING APPROACH .” 2012. Web. 30 Oct 2020.

Vancouver:

ZEWDIE M. OPTIMAL FEATURE SELECTION FOR NETWORK INTRUSION DETECTION: A DATA MINING APPROACH . [Internet] [Thesis]. Addis Ababa University; 2012. [cited 2020 Oct 30]. Available from: http://etd.aau.edu.et/dspace/handle/123456789/2836.

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

Council of Science Editors:

ZEWDIE M. OPTIMAL FEATURE SELECTION FOR NETWORK INTRUSION DETECTION: A DATA MINING APPROACH . [Thesis]. Addis Ababa University; 2012. Available from: http://etd.aau.edu.et/dspace/handle/123456789/2836

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


University of Rochester

10. Evans, Katie N.; Love, Tanzy. Extensions to model-based clustering for mixed-type data : a new model framework, variable selection, and outlier detection.

Degree: PhD, 2014, University of Rochester

 In many disciplines, such as marketing, biology, and bioinformatics, there is an increasing desire to identify distinct subgroups of observations within an observed data set;… (more)

Subjects/Keywords: Mixed-type data; Feature selection; Outliers; Clustering

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APA (6th Edition):

Evans, Katie N.; Love, T. (2014). Extensions to model-based clustering for mixed-type data : a new model framework, variable selection, and outlier detection. (Doctoral Dissertation). University of Rochester. Retrieved from http://hdl.handle.net/1802/28443

Chicago Manual of Style (16th Edition):

Evans, Katie N.; Love, Tanzy. “Extensions to model-based clustering for mixed-type data : a new model framework, variable selection, and outlier detection.” 2014. Doctoral Dissertation, University of Rochester. Accessed October 30, 2020. http://hdl.handle.net/1802/28443.

MLA Handbook (7th Edition):

Evans, Katie N.; Love, Tanzy. “Extensions to model-based clustering for mixed-type data : a new model framework, variable selection, and outlier detection.” 2014. Web. 30 Oct 2020.

Vancouver:

Evans, Katie N.; Love T. Extensions to model-based clustering for mixed-type data : a new model framework, variable selection, and outlier detection. [Internet] [Doctoral dissertation]. University of Rochester; 2014. [cited 2020 Oct 30]. Available from: http://hdl.handle.net/1802/28443.

Council of Science Editors:

Evans, Katie N.; Love T. Extensions to model-based clustering for mixed-type data : a new model framework, variable selection, and outlier detection. [Doctoral Dissertation]. University of Rochester; 2014. Available from: http://hdl.handle.net/1802/28443


Colorado State University

11. Mankovich, Nathan. Methods for network generation and spectral feature selection: especially on gene expression data.

Degree: MS(M.S.), Mathematics, 2019, Colorado State University

Feature selection is an essential step in many data analysis pipelines due to its ability to remove unimportant data. We will describe how to realize… (more)

Subjects/Keywords: feature selection; Laplacian; spectral; influenza; centrality; network

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

APA (6th Edition):

Mankovich, N. (2019). Methods for network generation and spectral feature selection: especially on gene expression data. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/199775

Chicago Manual of Style (16th Edition):

Mankovich, Nathan. “Methods for network generation and spectral feature selection: especially on gene expression data.” 2019. Masters Thesis, Colorado State University. Accessed October 30, 2020. http://hdl.handle.net/10217/199775.

MLA Handbook (7th Edition):

Mankovich, Nathan. “Methods for network generation and spectral feature selection: especially on gene expression data.” 2019. Web. 30 Oct 2020.

Vancouver:

Mankovich N. Methods for network generation and spectral feature selection: especially on gene expression data. [Internet] [Masters thesis]. Colorado State University; 2019. [cited 2020 Oct 30]. Available from: http://hdl.handle.net/10217/199775.

Council of Science Editors:

Mankovich N. Methods for network generation and spectral feature selection: especially on gene expression data. [Masters Thesis]. Colorado State University; 2019. Available from: http://hdl.handle.net/10217/199775


University of Connecticut

12. Yankee, Tara N. Rank Aggregation of Feature Scoring Methods for Unsupervised Learning.

Degree: M. Eng., Biomedical Engineering, 2017, University of Connecticut

  The ability to collect and store large amounts of data is transforming data-driven discovery; recent technological advances in biology allow systematic data production and… (more)

Subjects/Keywords: clustering; ensemble learning; feature selection; unsupervised learning

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APA (6th Edition):

Yankee, T. N. (2017). Rank Aggregation of Feature Scoring Methods for Unsupervised Learning. (Masters Thesis). University of Connecticut. Retrieved from https://opencommons.uconn.edu/gs_theses/1123

Chicago Manual of Style (16th Edition):

Yankee, Tara N. “Rank Aggregation of Feature Scoring Methods for Unsupervised Learning.” 2017. Masters Thesis, University of Connecticut. Accessed October 30, 2020. https://opencommons.uconn.edu/gs_theses/1123.

MLA Handbook (7th Edition):

Yankee, Tara N. “Rank Aggregation of Feature Scoring Methods for Unsupervised Learning.” 2017. Web. 30 Oct 2020.

Vancouver:

Yankee TN. Rank Aggregation of Feature Scoring Methods for Unsupervised Learning. [Internet] [Masters thesis]. University of Connecticut; 2017. [cited 2020 Oct 30]. Available from: https://opencommons.uconn.edu/gs_theses/1123.

Council of Science Editors:

Yankee TN. Rank Aggregation of Feature Scoring Methods for Unsupervised Learning. [Masters Thesis]. University of Connecticut; 2017. Available from: https://opencommons.uconn.edu/gs_theses/1123


University of Connecticut

13. McClanahan, Brian D. Location Inference of Social Media Posts at Hyper-Local Scale.

Degree: MS, Computer Science and Engineering, 2016, University of Connecticut

  This paper describes an approach to infer the location of a social media post at a hyper-local scale based on its content, conditional to… (more)

Subjects/Keywords: social media; feature selection; machine learning

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APA (6th Edition):

McClanahan, B. D. (2016). Location Inference of Social Media Posts at Hyper-Local Scale. (Masters Thesis). University of Connecticut. Retrieved from https://opencommons.uconn.edu/gs_theses/949

Chicago Manual of Style (16th Edition):

McClanahan, Brian D. “Location Inference of Social Media Posts at Hyper-Local Scale.” 2016. Masters Thesis, University of Connecticut. Accessed October 30, 2020. https://opencommons.uconn.edu/gs_theses/949.

MLA Handbook (7th Edition):

McClanahan, Brian D. “Location Inference of Social Media Posts at Hyper-Local Scale.” 2016. Web. 30 Oct 2020.

Vancouver:

McClanahan BD. Location Inference of Social Media Posts at Hyper-Local Scale. [Internet] [Masters thesis]. University of Connecticut; 2016. [cited 2020 Oct 30]. Available from: https://opencommons.uconn.edu/gs_theses/949.

Council of Science Editors:

McClanahan BD. Location Inference of Social Media Posts at Hyper-Local Scale. [Masters Thesis]. University of Connecticut; 2016. Available from: https://opencommons.uconn.edu/gs_theses/949


University of Illinois – Chicago

14. Wei, Xiaokai. Unsupervised Feature Selection for Heterogeneous Data.

Degree: 2017, University of Illinois – Chicago

 In the era of big data, one is often confronted with the problem of high-dimensional data in many data mining applications. Hence, feature selection has… (more)

Subjects/Keywords: Feature Selection; Heterogeneous Data; Information Network

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APA (6th Edition):

Wei, X. (2017). Unsupervised Feature Selection for Heterogeneous Data. (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/21855

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

Chicago Manual of Style (16th Edition):

Wei, Xiaokai. “Unsupervised Feature Selection for Heterogeneous Data.” 2017. Thesis, University of Illinois – Chicago. Accessed October 30, 2020. http://hdl.handle.net/10027/21855.

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

MLA Handbook (7th Edition):

Wei, Xiaokai. “Unsupervised Feature Selection for Heterogeneous Data.” 2017. Web. 30 Oct 2020.

Vancouver:

Wei X. Unsupervised Feature Selection for Heterogeneous Data. [Internet] [Thesis]. University of Illinois – Chicago; 2017. [cited 2020 Oct 30]. Available from: http://hdl.handle.net/10027/21855.

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

Council of Science Editors:

Wei X. Unsupervised Feature Selection for Heterogeneous Data. [Thesis]. University of Illinois – Chicago; 2017. Available from: http://hdl.handle.net/10027/21855

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


University of Manchester

15. Pocock, Adam Craig. Feature selection via joint likelihood.

Degree: PhD, 2012, University of Manchester

 We study the nature of filter methods for feature selection. In particular, we examine information theoretic approaches to this problem, looking at the literature over… (more)

Subjects/Keywords: 006.3; machine learning; feature selection; information theory

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

APA (6th Edition):

Pocock, A. C. (2012). Feature selection via joint likelihood. (Doctoral Dissertation). University of Manchester. Retrieved from https://www.research.manchester.ac.uk/portal/en/theses/feature-selection-via-joint-likelihood(3baba883-1fac-4658-bab0-164b54c3784a).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.558057

Chicago Manual of Style (16th Edition):

Pocock, Adam Craig. “Feature selection via joint likelihood.” 2012. Doctoral Dissertation, University of Manchester. Accessed October 30, 2020. https://www.research.manchester.ac.uk/portal/en/theses/feature-selection-via-joint-likelihood(3baba883-1fac-4658-bab0-164b54c3784a).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.558057.

MLA Handbook (7th Edition):

Pocock, Adam Craig. “Feature selection via joint likelihood.” 2012. Web. 30 Oct 2020.

Vancouver:

Pocock AC. Feature selection via joint likelihood. [Internet] [Doctoral dissertation]. University of Manchester; 2012. [cited 2020 Oct 30]. Available from: https://www.research.manchester.ac.uk/portal/en/theses/feature-selection-via-joint-likelihood(3baba883-1fac-4658-bab0-164b54c3784a).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.558057.

Council of Science Editors:

Pocock AC. Feature selection via joint likelihood. [Doctoral Dissertation]. University of Manchester; 2012. Available from: https://www.research.manchester.ac.uk/portal/en/theses/feature-selection-via-joint-likelihood(3baba883-1fac-4658-bab0-164b54c3784a).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.558057


University of Victoria

16. Sharma, Mridula. Evaluating and enhancing the security of cyber physical systems using machine learning approaches.

Degree: Department of Electrical and Computer Engineering, 2020, University of Victoria

 The main aim of this dissertation is to address the security issues of the physical layer of Cyber Physical Systems. The network security is first… (more)

Subjects/Keywords: CPS; Supervised Machine Learning; RPL; Feature Selection

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

APA (6th Edition):

Sharma, M. (2020). Evaluating and enhancing the security of cyber physical systems using machine learning approaches. (Thesis). University of Victoria. Retrieved from http://hdl.handle.net/1828/11675

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

Chicago Manual of Style (16th Edition):

Sharma, Mridula. “Evaluating and enhancing the security of cyber physical systems using machine learning approaches.” 2020. Thesis, University of Victoria. Accessed October 30, 2020. http://hdl.handle.net/1828/11675.

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

MLA Handbook (7th Edition):

Sharma, Mridula. “Evaluating and enhancing the security of cyber physical systems using machine learning approaches.” 2020. Web. 30 Oct 2020.

Vancouver:

Sharma M. Evaluating and enhancing the security of cyber physical systems using machine learning approaches. [Internet] [Thesis]. University of Victoria; 2020. [cited 2020 Oct 30]. Available from: http://hdl.handle.net/1828/11675.

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

Council of Science Editors:

Sharma M. Evaluating and enhancing the security of cyber physical systems using machine learning approaches. [Thesis]. University of Victoria; 2020. Available from: http://hdl.handle.net/1828/11675

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


University of Southern California

17. Tsau, Enshuo. Advanced features and feature selection methods for vibration and audio signal classification.

Degree: PhD, Electrical Engineering, 2012, University of Southern California

 An adequate feature set plays a key role in many signal classification and recognition applications. This is a challenging problem due to the nonlinearity and… (more)

Subjects/Keywords: CELP; fault diagnosis; feature selection; HHT

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

APA (6th Edition):

Tsau, E. (2012). Advanced features and feature selection methods for vibration and audio signal classification. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/89597/rec/542

Chicago Manual of Style (16th Edition):

Tsau, Enshuo. “Advanced features and feature selection methods for vibration and audio signal classification.” 2012. Doctoral Dissertation, University of Southern California. Accessed October 30, 2020. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/89597/rec/542.

MLA Handbook (7th Edition):

Tsau, Enshuo. “Advanced features and feature selection methods for vibration and audio signal classification.” 2012. Web. 30 Oct 2020.

Vancouver:

Tsau E. Advanced features and feature selection methods for vibration and audio signal classification. [Internet] [Doctoral dissertation]. University of Southern California; 2012. [cited 2020 Oct 30]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/89597/rec/542.

Council of Science Editors:

Tsau E. Advanced features and feature selection methods for vibration and audio signal classification. [Doctoral Dissertation]. University of Southern California; 2012. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/89597/rec/542


University of Southern California

18. Cho, Seong Ho. Block-based image steganalysis: algorithm and performance evaluation.

Degree: PhD, Electrical Engineering, 2012, University of Southern California

 Traditional image steganalysis techniques are conducted with respect to the entire image. In this work, we aim to differentiate a stego image from its cover… (more)

Subjects/Keywords: steganalysis; steganography; decision fusion; feature selection

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APA (6th Edition):

Cho, S. H. (2012). Block-based image steganalysis: algorithm and performance evaluation. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/81733/rec/1142

Chicago Manual of Style (16th Edition):

Cho, Seong Ho. “Block-based image steganalysis: algorithm and performance evaluation.” 2012. Doctoral Dissertation, University of Southern California. Accessed October 30, 2020. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/81733/rec/1142.

MLA Handbook (7th Edition):

Cho, Seong Ho. “Block-based image steganalysis: algorithm and performance evaluation.” 2012. Web. 30 Oct 2020.

Vancouver:

Cho SH. Block-based image steganalysis: algorithm and performance evaluation. [Internet] [Doctoral dissertation]. University of Southern California; 2012. [cited 2020 Oct 30]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/81733/rec/1142.

Council of Science Editors:

Cho SH. Block-based image steganalysis: algorithm and performance evaluation. [Doctoral Dissertation]. University of Southern California; 2012. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/81733/rec/1142


University of Kansas

19. Zhong, Yi. Feature selection and classification for high-dimensional biological data under cross-validation framework.

Degree: PhD, Biostatistics, 2018, University of Kansas

 This research focuses on using statistical learning methods on high-dimensional biological data analysis. In our implementation of high-dimensional biological data analysis, we primarily utilize the… (more)

Subjects/Keywords: Statistics; cross-validation; feature selection; statistical learning

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

APA (6th Edition):

Zhong, Y. (2018). Feature selection and classification for high-dimensional biological data under cross-validation framework. (Doctoral Dissertation). University of Kansas. Retrieved from http://hdl.handle.net/1808/27072

Chicago Manual of Style (16th Edition):

Zhong, Yi. “Feature selection and classification for high-dimensional biological data under cross-validation framework.” 2018. Doctoral Dissertation, University of Kansas. Accessed October 30, 2020. http://hdl.handle.net/1808/27072.

MLA Handbook (7th Edition):

Zhong, Yi. “Feature selection and classification for high-dimensional biological data under cross-validation framework.” 2018. Web. 30 Oct 2020.

Vancouver:

Zhong Y. Feature selection and classification for high-dimensional biological data under cross-validation framework. [Internet] [Doctoral dissertation]. University of Kansas; 2018. [cited 2020 Oct 30]. Available from: http://hdl.handle.net/1808/27072.

Council of Science Editors:

Zhong Y. Feature selection and classification for high-dimensional biological data under cross-validation framework. [Doctoral Dissertation]. University of Kansas; 2018. Available from: http://hdl.handle.net/1808/27072

20. Andreasson, Philip. Feature selection in an industrial data set .

Degree: Chalmers tekniska högskola / Institutionen för fysik, 2019, Chalmers University of Technology

Feature selection is a technique for reducing the dimensionality of data sets which can provide benefits in terms of computational time, performance and interpretability. This… (more)

Subjects/Keywords: feature selection; genetic algorithms; categorical features

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APA (6th Edition):

Andreasson, P. (2019). Feature selection in an industrial data set . (Thesis). Chalmers University of Technology. Retrieved from http://hdl.handle.net/20.500.12380/300642

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

Chicago Manual of Style (16th Edition):

Andreasson, Philip. “Feature selection in an industrial data set .” 2019. Thesis, Chalmers University of Technology. Accessed October 30, 2020. http://hdl.handle.net/20.500.12380/300642.

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

MLA Handbook (7th Edition):

Andreasson, Philip. “Feature selection in an industrial data set .” 2019. Web. 30 Oct 2020.

Vancouver:

Andreasson P. Feature selection in an industrial data set . [Internet] [Thesis]. Chalmers University of Technology; 2019. [cited 2020 Oct 30]. Available from: http://hdl.handle.net/20.500.12380/300642.

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

Council of Science Editors:

Andreasson P. Feature selection in an industrial data set . [Thesis]. Chalmers University of Technology; 2019. Available from: http://hdl.handle.net/20.500.12380/300642

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


University of Sydney

21. Pham, Thuy Thi. Automated Classification for Biomedical Data: Machine Learning Approaches for Subject-Independent Settings .

Degree: 2017, University of Sydney

 This thesis advocates two main factors to improve subject-independent automated classification techniques: utilizing better feature extraction, and a more efficient model of classification. Supervised techniques… (more)

Subjects/Keywords: Subject-independent; automated classification; feature selection; anomaly

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APA (6th Edition):

Pham, T. T. (2017). Automated Classification for Biomedical Data: Machine Learning Approaches for Subject-Independent Settings . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/17177

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

Chicago Manual of Style (16th Edition):

Pham, Thuy Thi. “Automated Classification for Biomedical Data: Machine Learning Approaches for Subject-Independent Settings .” 2017. Thesis, University of Sydney. Accessed October 30, 2020. http://hdl.handle.net/2123/17177.

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

MLA Handbook (7th Edition):

Pham, Thuy Thi. “Automated Classification for Biomedical Data: Machine Learning Approaches for Subject-Independent Settings .” 2017. Web. 30 Oct 2020.

Vancouver:

Pham TT. Automated Classification for Biomedical Data: Machine Learning Approaches for Subject-Independent Settings . [Internet] [Thesis]. University of Sydney; 2017. [cited 2020 Oct 30]. Available from: http://hdl.handle.net/2123/17177.

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

Council of Science Editors:

Pham TT. Automated Classification for Biomedical Data: Machine Learning Approaches for Subject-Independent Settings . [Thesis]. University of Sydney; 2017. Available from: http://hdl.handle.net/2123/17177

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


University of Cincinnati

22. Zhang, Yi. Application of Hyper-geometric Hypothesis-based Quantication and Markov Blanket Feature Selection Methods to Generate Signals for Adverse Drug Reaction Detection.

Degree: MS, Engineering and Applied Science: Mechanical Engineering, 2012, University of Cincinnati

 Pharmacovigilance is the science relating to all concerns about drug safety, especially ofmanaging the risk associated with medications. It serves as a complementary approach toclinical… (more)

Subjects/Keywords: Mechanical Engineering; Pharmacovigilance; Data Mining; Feature Selection

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APA (6th Edition):

Zhang, Y. (2012). Application of Hyper-geometric Hypothesis-based Quantication and Markov Blanket Feature Selection Methods to Generate Signals for Adverse Drug Reaction Detection. (Masters Thesis). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1353343669

Chicago Manual of Style (16th Edition):

Zhang, Yi. “Application of Hyper-geometric Hypothesis-based Quantication and Markov Blanket Feature Selection Methods to Generate Signals for Adverse Drug Reaction Detection.” 2012. Masters Thesis, University of Cincinnati. Accessed October 30, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1353343669.

MLA Handbook (7th Edition):

Zhang, Yi. “Application of Hyper-geometric Hypothesis-based Quantication and Markov Blanket Feature Selection Methods to Generate Signals for Adverse Drug Reaction Detection.” 2012. Web. 30 Oct 2020.

Vancouver:

Zhang Y. Application of Hyper-geometric Hypothesis-based Quantication and Markov Blanket Feature Selection Methods to Generate Signals for Adverse Drug Reaction Detection. [Internet] [Masters thesis]. University of Cincinnati; 2012. [cited 2020 Oct 30]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1353343669.

Council of Science Editors:

Zhang Y. Application of Hyper-geometric Hypothesis-based Quantication and Markov Blanket Feature Selection Methods to Generate Signals for Adverse Drug Reaction Detection. [Masters Thesis]. University of Cincinnati; 2012. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1353343669


Oklahoma State University

23. Dai, Qingqing. Feature selection and personalized modeling on medical adverse outcome prediction.

Degree: Statistics, 2020, Oklahoma State University

 This thesis is about the medical adverse outcome prediction and is composed of three parts, i.e. feature selection, time-to-event prediction and personalized modeling. For feature(more)

Subjects/Keywords: adverse outcome; feature selection; personalized modeling; prediction

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APA (6th Edition):

Dai, Q. (2020). Feature selection and personalized modeling on medical adverse outcome prediction. (Thesis). Oklahoma State University. Retrieved from http://hdl.handle.net/11244/325459

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

Chicago Manual of Style (16th Edition):

Dai, Qingqing. “Feature selection and personalized modeling on medical adverse outcome prediction.” 2020. Thesis, Oklahoma State University. Accessed October 30, 2020. http://hdl.handle.net/11244/325459.

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

MLA Handbook (7th Edition):

Dai, Qingqing. “Feature selection and personalized modeling on medical adverse outcome prediction.” 2020. Web. 30 Oct 2020.

Vancouver:

Dai Q. Feature selection and personalized modeling on medical adverse outcome prediction. [Internet] [Thesis]. Oklahoma State University; 2020. [cited 2020 Oct 30]. Available from: http://hdl.handle.net/11244/325459.

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

Council of Science Editors:

Dai Q. Feature selection and personalized modeling on medical adverse outcome prediction. [Thesis]. Oklahoma State University; 2020. Available from: http://hdl.handle.net/11244/325459

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


University of New South Wales

24. Hossain, Md Ali. Subspace Detection Approaches for Hyperspectral Image Classification.

Degree: Engineering & Information Technology Canberra, 2014, University of New South Wales

 Hyperspectral data provides rich information and is very useful for a range of applications from ground-cover types identification to target detection. With many benefits they… (more)

Subjects/Keywords: Feature selection; Image classification; Feature extraction; Mutual Information; Hyperspectral image; Feature reduction

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APA (6th Edition):

Hossain, M. A. (2014). Subspace Detection Approaches for Hyperspectral Image Classification. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/53507 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12202/SOURCE02?view=true

Chicago Manual of Style (16th Edition):

Hossain, Md Ali. “Subspace Detection Approaches for Hyperspectral Image Classification.” 2014. Doctoral Dissertation, University of New South Wales. Accessed October 30, 2020. http://handle.unsw.edu.au/1959.4/53507 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12202/SOURCE02?view=true.

MLA Handbook (7th Edition):

Hossain, Md Ali. “Subspace Detection Approaches for Hyperspectral Image Classification.” 2014. Web. 30 Oct 2020.

Vancouver:

Hossain MA. Subspace Detection Approaches for Hyperspectral Image Classification. [Internet] [Doctoral dissertation]. University of New South Wales; 2014. [cited 2020 Oct 30]. Available from: http://handle.unsw.edu.au/1959.4/53507 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12202/SOURCE02?view=true.

Council of Science Editors:

Hossain MA. Subspace Detection Approaches for Hyperspectral Image Classification. [Doctoral Dissertation]. University of New South Wales; 2014. Available from: http://handle.unsw.edu.au/1959.4/53507 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12202/SOURCE02?view=true


NSYSU

25. Wang, Po-Cheng. Automatic Attribute Clustering and Feature Selection Based on Genetic Algorithms.

Degree: Master, Computer Science and Engineering, 2009, NSYSU

Feature selection is an important pre-processing step in mining and learning. A good set of features can not only improve the accuracy of classification, but… (more)

Subjects/Keywords: k-means; reduct; genetic algorithms; feature clustering; feature selection

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APA (6th Edition):

Wang, P. (2009). Automatic Attribute Clustering and Feature Selection Based on Genetic Algorithms. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0821109-092325

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

Chicago Manual of Style (16th Edition):

Wang, Po-Cheng. “Automatic Attribute Clustering and Feature Selection Based on Genetic Algorithms.” 2009. Thesis, NSYSU. Accessed October 30, 2020. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0821109-092325.

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

MLA Handbook (7th Edition):

Wang, Po-Cheng. “Automatic Attribute Clustering and Feature Selection Based on Genetic Algorithms.” 2009. Web. 30 Oct 2020.

Vancouver:

Wang P. Automatic Attribute Clustering and Feature Selection Based on Genetic Algorithms. [Internet] [Thesis]. NSYSU; 2009. [cited 2020 Oct 30]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0821109-092325.

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

Council of Science Editors:

Wang P. Automatic Attribute Clustering and Feature Selection Based on Genetic Algorithms. [Thesis]. NSYSU; 2009. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0821109-092325

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


Rochester Institute of Technology

26. Stelmack, John. Kaizen Programming with Enhanced Feature Discovery: An Automated Approach to Feature Selection and Feature Discovery for Prediction Models.

Degree: MS, Industrial and Systems Engineering, 2020, Rochester Institute of Technology

Feature selection (FS) is the process of finding an ideal set of features for a prediction model from a set of candidate features. A… (more)

Subjects/Keywords: Feature engineering; Feature selection; Forecasting; Genetic programming; Prediction algorithm; Regression

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APA (6th Edition):

Stelmack, J. (2020). Kaizen Programming with Enhanced Feature Discovery: An Automated Approach to Feature Selection and Feature Discovery for Prediction Models. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/10419

Chicago Manual of Style (16th Edition):

Stelmack, John. “Kaizen Programming with Enhanced Feature Discovery: An Automated Approach to Feature Selection and Feature Discovery for Prediction Models.” 2020. Masters Thesis, Rochester Institute of Technology. Accessed October 30, 2020. https://scholarworks.rit.edu/theses/10419.

MLA Handbook (7th Edition):

Stelmack, John. “Kaizen Programming with Enhanced Feature Discovery: An Automated Approach to Feature Selection and Feature Discovery for Prediction Models.” 2020. Web. 30 Oct 2020.

Vancouver:

Stelmack J. Kaizen Programming with Enhanced Feature Discovery: An Automated Approach to Feature Selection and Feature Discovery for Prediction Models. [Internet] [Masters thesis]. Rochester Institute of Technology; 2020. [cited 2020 Oct 30]. Available from: https://scholarworks.rit.edu/theses/10419.

Council of Science Editors:

Stelmack J. Kaizen Programming with Enhanced Feature Discovery: An Automated Approach to Feature Selection and Feature Discovery for Prediction Models. [Masters Thesis]. Rochester Institute of Technology; 2020. Available from: https://scholarworks.rit.edu/theses/10419


University of Newcastle

27. Rocha de Paula, Mateus. Efficient methods of feature selection based on combinatorial optimization motivated by the analysis of large biological datasets.

Degree: PhD, 2013, University of Newcastle

Research Doctorate - Doctor of Philosophy (PhD)

Intuitively, the Feature Selection problem is to choose a subset of a given a set of features that… (more)

Subjects/Keywords: Feature Selection; combinatorial optimization; heuristics; bioinformatics; (a,b)-k-Feature Set

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

APA (6th Edition):

Rocha de Paula, M. (2013). Efficient methods of feature selection based on combinatorial optimization motivated by the analysis of large biological datasets. (Doctoral Dissertation). University of Newcastle. Retrieved from http://hdl.handle.net/1959.13/938563

Chicago Manual of Style (16th Edition):

Rocha de Paula, Mateus. “Efficient methods of feature selection based on combinatorial optimization motivated by the analysis of large biological datasets.” 2013. Doctoral Dissertation, University of Newcastle. Accessed October 30, 2020. http://hdl.handle.net/1959.13/938563.

MLA Handbook (7th Edition):

Rocha de Paula, Mateus. “Efficient methods of feature selection based on combinatorial optimization motivated by the analysis of large biological datasets.” 2013. Web. 30 Oct 2020.

Vancouver:

Rocha de Paula M. Efficient methods of feature selection based on combinatorial optimization motivated by the analysis of large biological datasets. [Internet] [Doctoral dissertation]. University of Newcastle; 2013. [cited 2020 Oct 30]. Available from: http://hdl.handle.net/1959.13/938563.

Council of Science Editors:

Rocha de Paula M. Efficient methods of feature selection based on combinatorial optimization motivated by the analysis of large biological datasets. [Doctoral Dissertation]. University of Newcastle; 2013. Available from: http://hdl.handle.net/1959.13/938563


Victoria University of Wellington

28. Tran, Binh Ngan. Evolutionary Computation for Feature Manipulation in Classification on High-dimensional Data.

Degree: 2018, Victoria University of Wellington

 More and more high-dimensional data appears in machine learning, especially in classification tasks. With thousands of features, these datasets bring challenges to learning algorithms not… (more)

Subjects/Keywords: Evolutionary Computation; Feature selection; Feature construction; Classification; High-dimensional data

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

APA (6th Edition):

Tran, B. N. (2018). Evolutionary Computation for Feature Manipulation in Classification on High-dimensional Data. (Doctoral Dissertation). Victoria University of Wellington. Retrieved from http://hdl.handle.net/10063/7078

Chicago Manual of Style (16th Edition):

Tran, Binh Ngan. “Evolutionary Computation for Feature Manipulation in Classification on High-dimensional Data.” 2018. Doctoral Dissertation, Victoria University of Wellington. Accessed October 30, 2020. http://hdl.handle.net/10063/7078.

MLA Handbook (7th Edition):

Tran, Binh Ngan. “Evolutionary Computation for Feature Manipulation in Classification on High-dimensional Data.” 2018. Web. 30 Oct 2020.

Vancouver:

Tran BN. Evolutionary Computation for Feature Manipulation in Classification on High-dimensional Data. [Internet] [Doctoral dissertation]. Victoria University of Wellington; 2018. [cited 2020 Oct 30]. Available from: http://hdl.handle.net/10063/7078.

Council of Science Editors:

Tran BN. Evolutionary Computation for Feature Manipulation in Classification on High-dimensional Data. [Doctoral Dissertation]. Victoria University of Wellington; 2018. Available from: http://hdl.handle.net/10063/7078


Victoria University of Wellington

29. Neshatian, Kourosh. Feature Manipulation with Genetic Programming.

Degree: 2010, Victoria University of Wellington

Feature manipulation refers to the process by which the input space of a machine learning task is altered in order to improve the learning quality… (more)

Subjects/Keywords: Machine learning; Evolutionary algorithms; Feature selection; Feature construction

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APA (6th Edition):

Neshatian, K. (2010). Feature Manipulation with Genetic Programming. (Doctoral Dissertation). Victoria University of Wellington. Retrieved from http://hdl.handle.net/10063/4425

Chicago Manual of Style (16th Edition):

Neshatian, Kourosh. “Feature Manipulation with Genetic Programming.” 2010. Doctoral Dissertation, Victoria University of Wellington. Accessed October 30, 2020. http://hdl.handle.net/10063/4425.

MLA Handbook (7th Edition):

Neshatian, Kourosh. “Feature Manipulation with Genetic Programming.” 2010. Web. 30 Oct 2020.

Vancouver:

Neshatian K. Feature Manipulation with Genetic Programming. [Internet] [Doctoral dissertation]. Victoria University of Wellington; 2010. [cited 2020 Oct 30]. Available from: http://hdl.handle.net/10063/4425.

Council of Science Editors:

Neshatian K. Feature Manipulation with Genetic Programming. [Doctoral Dissertation]. Victoria University of Wellington; 2010. Available from: http://hdl.handle.net/10063/4425


Delft University of Technology

30. Wang, Yuyang (author). An automated ECG signal quality assessment method with supervised learning algorithm.

Degree: 2018, Delft University of Technology

 Wearable health has become a striking area in our daily life. Electrocardiogram (ECG) is one of the biomedical signals collected by the wearable or portable… (more)

Subjects/Keywords: ECG signal; quality assessment; Supervised Learning; Feature extraction; Feature selection

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

APA (6th Edition):

Wang, Y. (. (2018). An automated ECG signal quality assessment method with supervised learning algorithm. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:41eab0f1-05a5-440c-a43b-f2f12b15a8aa

Chicago Manual of Style (16th Edition):

Wang, Yuyang (author). “An automated ECG signal quality assessment method with supervised learning algorithm.” 2018. Masters Thesis, Delft University of Technology. Accessed October 30, 2020. http://resolver.tudelft.nl/uuid:41eab0f1-05a5-440c-a43b-f2f12b15a8aa.

MLA Handbook (7th Edition):

Wang, Yuyang (author). “An automated ECG signal quality assessment method with supervised learning algorithm.” 2018. Web. 30 Oct 2020.

Vancouver:

Wang Y(. An automated ECG signal quality assessment method with supervised learning algorithm. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2020 Oct 30]. Available from: http://resolver.tudelft.nl/uuid:41eab0f1-05a5-440c-a43b-f2f12b15a8aa.

Council of Science Editors:

Wang Y(. An automated ECG signal quality assessment method with supervised learning algorithm. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:41eab0f1-05a5-440c-a43b-f2f12b15a8aa

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