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You searched for subject:(Feature selection). Showing records 1 – 11 of 11 total matches.

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Victoria University of Wellington

1. Nguyen, Bach Hoai. Evolutionary Computation for Feature Selection in Classification.

Degree: 2018, Victoria University of Wellington

 Classification aims to identify a class label of an instance according to the information from its characteristics or features. Unfortunately, many classification problems have a… (more)

Subjects/Keywords: Feature Selection; Evolutionary Computation; Classification

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APA (6th Edition):

Nguyen, B. H. (2018). Evolutionary Computation for Feature Selection in Classification. (Doctoral Dissertation). Victoria University of Wellington. Retrieved from http://hdl.handle.net/10063/7821

Chicago Manual of Style (16th Edition):

Nguyen, Bach Hoai. “Evolutionary Computation for Feature Selection in Classification.” 2018. Doctoral Dissertation, Victoria University of Wellington. Accessed June 25, 2019. http://hdl.handle.net/10063/7821.

MLA Handbook (7th Edition):

Nguyen, Bach Hoai. “Evolutionary Computation for Feature Selection in Classification.” 2018. Web. 25 Jun 2019.

Vancouver:

Nguyen BH. Evolutionary Computation for Feature Selection in Classification. [Internet] [Doctoral dissertation]. Victoria University of Wellington; 2018. [cited 2019 Jun 25]. Available from: http://hdl.handle.net/10063/7821.

Council of Science Editors:

Nguyen BH. Evolutionary Computation for Feature Selection in Classification. [Doctoral Dissertation]. Victoria University of Wellington; 2018. Available from: http://hdl.handle.net/10063/7821


Victoria University of Wellington

2. 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 (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 June 25, 2019. 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. 25 Jun 2019.

Vancouver:

Tran BN. Evolutionary Computation for Feature Manipulation in Classification on High-dimensional Data. [Internet] [Doctoral dissertation]. Victoria University of Wellington; 2018. [cited 2019 Jun 25]. 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

3. 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 June 25, 2019. http://hdl.handle.net/10063/4425.

MLA Handbook (7th Edition):

Neshatian, Kourosh. “Feature Manipulation with Genetic Programming.” 2010. Web. 25 Jun 2019.

Vancouver:

Neshatian K. Feature Manipulation with Genetic Programming. [Internet] [Doctoral dissertation]. Victoria University of Wellington; 2010. [cited 2019 Jun 25]. 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


AUT University

4. Liang, Wen. Integrated feature, neighbourhood, and model optimization for personalised modelling and knowledge discovery .

Degree: 2009, AUT University

 “Machine learning is the process of discovering and interpreting meaningful information, such as new correlations, patterns and trends by sifting through large amounts of data… (more)

Subjects/Keywords: Optimisation; Personalised modelling; Feature selection; Nearest neighbour; Model optimization

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APA (6th Edition):

Liang, W. (2009). Integrated feature, neighbourhood, and model optimization for personalised modelling and knowledge discovery . (Thesis). AUT University. Retrieved from http://hdl.handle.net/10292/749

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):

Liang, Wen. “Integrated feature, neighbourhood, and model optimization for personalised modelling and knowledge discovery .” 2009. Thesis, AUT University. Accessed June 25, 2019. http://hdl.handle.net/10292/749.

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

MLA Handbook (7th Edition):

Liang, Wen. “Integrated feature, neighbourhood, and model optimization for personalised modelling and knowledge discovery .” 2009. Web. 25 Jun 2019.

Vancouver:

Liang W. Integrated feature, neighbourhood, and model optimization for personalised modelling and knowledge discovery . [Internet] [Thesis]. AUT University; 2009. [cited 2019 Jun 25]. Available from: http://hdl.handle.net/10292/749.

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

Council of Science Editors:

Liang W. Integrated feature, neighbourhood, and model optimization for personalised modelling and knowledge discovery . [Thesis]. AUT University; 2009. Available from: http://hdl.handle.net/10292/749

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


AUT University

5. Hu, Yingjie. Personalised modelling framework and systems for Gene Data analysis and Biomedical applications .

Degree: 2011, AUT University

 The core focus of this research is at the development of novel information methods and systems based on personalised modelling for genomic data analysis and… (more)

Subjects/Keywords: Personalised Modelling; Gene data analysis; Biomedical applications; Evolutionary computing; Feature selection

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APA (6th Edition):

Hu, Y. (2011). Personalised modelling framework and systems for Gene Data analysis and Biomedical applications . (Thesis). AUT University. Retrieved from http://hdl.handle.net/10292/1159

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):

Hu, Yingjie. “Personalised modelling framework and systems for Gene Data analysis and Biomedical applications .” 2011. Thesis, AUT University. Accessed June 25, 2019. http://hdl.handle.net/10292/1159.

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

MLA Handbook (7th Edition):

Hu, Yingjie. “Personalised modelling framework and systems for Gene Data analysis and Biomedical applications .” 2011. Web. 25 Jun 2019.

Vancouver:

Hu Y. Personalised modelling framework and systems for Gene Data analysis and Biomedical applications . [Internet] [Thesis]. AUT University; 2011. [cited 2019 Jun 25]. Available from: http://hdl.handle.net/10292/1159.

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

Council of Science Editors:

Hu Y. Personalised modelling framework and systems for Gene Data analysis and Biomedical applications . [Thesis]. AUT University; 2011. Available from: http://hdl.handle.net/10292/1159

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


University of Waikato

6. Pradhananga, Nripendra. Effective Linear-Time Feature Selection .

Degree: 2007, University of Waikato

 The classification learning task requires selection of a subset of features to represent patterns to be classified. This is because the performance of the classifier… (more)

Subjects/Keywords: filter; wrapper; feature selection; attribute selection; ensemble learning; machine learning; Linear Feature Selection

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APA (6th Edition):

Pradhananga, N. (2007). Effective Linear-Time Feature Selection . (Masters Thesis). University of Waikato. Retrieved from http://hdl.handle.net/10289/2315

Chicago Manual of Style (16th Edition):

Pradhananga, Nripendra. “Effective Linear-Time Feature Selection .” 2007. Masters Thesis, University of Waikato. Accessed June 25, 2019. http://hdl.handle.net/10289/2315.

MLA Handbook (7th Edition):

Pradhananga, Nripendra. “Effective Linear-Time Feature Selection .” 2007. Web. 25 Jun 2019.

Vancouver:

Pradhananga N. Effective Linear-Time Feature Selection . [Internet] [Masters thesis]. University of Waikato; 2007. [cited 2019 Jun 25]. Available from: http://hdl.handle.net/10289/2315.

Council of Science Editors:

Pradhananga N. Effective Linear-Time Feature Selection . [Masters Thesis]. University of Waikato; 2007. Available from: http://hdl.handle.net/10289/2315


Victoria University of Wellington

7. Tran, Cao Truong. Evolutionary Machine Learning for Classification with Incomplete Data.

Degree: 2018, Victoria University of Wellington

 Classification is a major task in machine learning and data mining. Many real-world datasets suffer from the unavoidable issue of missing values. Classification with incomplete… (more)

Subjects/Keywords: Incomplete data; Missing data; Classification; Machine learning; Evolutionary computation; Genetic programming; Ensemble learning; Feature selection; Feature construction

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

APA (6th Edition):

Tran, C. T. (2018). Evolutionary Machine Learning for Classification with Incomplete Data. (Doctoral Dissertation). Victoria University of Wellington. Retrieved from http://hdl.handle.net/10063/7639

Chicago Manual of Style (16th Edition):

Tran, Cao Truong. “Evolutionary Machine Learning for Classification with Incomplete Data.” 2018. Doctoral Dissertation, Victoria University of Wellington. Accessed June 25, 2019. http://hdl.handle.net/10063/7639.

MLA Handbook (7th Edition):

Tran, Cao Truong. “Evolutionary Machine Learning for Classification with Incomplete Data.” 2018. Web. 25 Jun 2019.

Vancouver:

Tran CT. Evolutionary Machine Learning for Classification with Incomplete Data. [Internet] [Doctoral dissertation]. Victoria University of Wellington; 2018. [cited 2019 Jun 25]. Available from: http://hdl.handle.net/10063/7639.

Council of Science Editors:

Tran CT. Evolutionary Machine Learning for Classification with Incomplete Data. [Doctoral Dissertation]. Victoria University of Wellington; 2018. Available from: http://hdl.handle.net/10063/7639


AUT University

8. Abdull Hamed, Haza Nuzly. Novel Integrated Methods of Evolving Spiking Neural Network and Particle Swarm Optimisation .

Degree: 2012, AUT University

 This thesis proposes and presents several methods for classification problems. Spatial and spatiotemporal classification problems have been considered in this study. A novel integration between… (more)

Subjects/Keywords: Spiking Neural Network; Particle Swarm Optimisation; Evolving Connectionist System; Quantum Computation; Liquid State Machine; Classification; Spatiotemporal; Spatial; Feature Selection; Parameter Optimisation

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APA (6th Edition):

Abdull Hamed, H. N. (2012). Novel Integrated Methods of Evolving Spiking Neural Network and Particle Swarm Optimisation . (Thesis). AUT University. Retrieved from http://hdl.handle.net/10292/4459

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):

Abdull Hamed, Haza Nuzly. “Novel Integrated Methods of Evolving Spiking Neural Network and Particle Swarm Optimisation .” 2012. Thesis, AUT University. Accessed June 25, 2019. http://hdl.handle.net/10292/4459.

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

MLA Handbook (7th Edition):

Abdull Hamed, Haza Nuzly. “Novel Integrated Methods of Evolving Spiking Neural Network and Particle Swarm Optimisation .” 2012. Web. 25 Jun 2019.

Vancouver:

Abdull Hamed HN. Novel Integrated Methods of Evolving Spiking Neural Network and Particle Swarm Optimisation . [Internet] [Thesis]. AUT University; 2012. [cited 2019 Jun 25]. Available from: http://hdl.handle.net/10292/4459.

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

Council of Science Editors:

Abdull Hamed HN. Novel Integrated Methods of Evolving Spiking Neural Network and Particle Swarm Optimisation . [Thesis]. AUT University; 2012. Available from: http://hdl.handle.net/10292/4459

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

9. Xue, Bing. Particle Swarm Optimisation for Feature Selection in Classification.

Degree: 2014, Victoria University of Wellington

 Classification problems often have a large number of features, but not all of them are useful for classification. Irrelevant and redundant features may even reduce… (more)

Subjects/Keywords: Feature selection; Particle swarm optimisation; Classification

…Challenges of Feature Selection . . . . . . . . . . . . . 3 1.2.2 Why PSO… …21 ix x CONTENTS 2.2 2.3 2.4 2.5 Feature Selection… …25 2.2.1 General Feature Selection Process . . . . . . . . . . . 26 2.2.2 Filter vs… …for Feature Selection . . . . . . . . . . . 50 2.6.1 Wrapper Feature Selection Approaches… …50 2.6.2 Filter Feature Selection Approaches . . . . . . . . . . 52 EC Techniques for… 

Page 1 Page 2 Page 3 Page 4 Page 5 Page 6 Page 7

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

APA (6th Edition):

Xue, B. (2014). Particle Swarm Optimisation for Feature Selection in Classification. (Doctoral Dissertation). Victoria University of Wellington. Retrieved from http://hdl.handle.net/10063/3198

Chicago Manual of Style (16th Edition):

Xue, Bing. “Particle Swarm Optimisation for Feature Selection in Classification.” 2014. Doctoral Dissertation, Victoria University of Wellington. Accessed June 25, 2019. http://hdl.handle.net/10063/3198.

MLA Handbook (7th Edition):

Xue, Bing. “Particle Swarm Optimisation for Feature Selection in Classification.” 2014. Web. 25 Jun 2019.

Vancouver:

Xue B. Particle Swarm Optimisation for Feature Selection in Classification. [Internet] [Doctoral dissertation]. Victoria University of Wellington; 2014. [cited 2019 Jun 25]. Available from: http://hdl.handle.net/10063/3198.

Council of Science Editors:

Xue B. Particle Swarm Optimisation for Feature Selection in Classification. [Doctoral Dissertation]. Victoria University of Wellington; 2014. Available from: http://hdl.handle.net/10063/3198


Unitec New Zealand

10. Ho, Trung Minh. Evaluating spammer detection systems for Twitter.

Degree: 2017, Unitec New Zealand

 Twitter is a popular Social Network Service. It is a web application with dual roles of online social network and microblogging. Users use Twitter to… (more)

Subjects/Keywords: Twitter; spam detection; spam drift; optimisation subset of features; evaluation workbench; feature selection; machine learning; 080303 Computer System Security; 080109 Pattern Recognition and Data Mining

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APA (6th Edition):

Ho, T. M. (2017). Evaluating spammer detection systems for Twitter. (Thesis). Unitec New Zealand. Retrieved from http://hdl.handle.net/10652/4525

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):

Ho, Trung Minh. “Evaluating spammer detection systems for Twitter.” 2017. Thesis, Unitec New Zealand. Accessed June 25, 2019. http://hdl.handle.net/10652/4525.

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

MLA Handbook (7th Edition):

Ho, Trung Minh. “Evaluating spammer detection systems for Twitter.” 2017. Web. 25 Jun 2019.

Vancouver:

Ho TM. Evaluating spammer detection systems for Twitter. [Internet] [Thesis]. Unitec New Zealand; 2017. [cited 2019 Jun 25]. Available from: http://hdl.handle.net/10652/4525.

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

Council of Science Editors:

Ho TM. Evaluating spammer detection systems for Twitter. [Thesis]. Unitec New Zealand; 2017. Available from: http://hdl.handle.net/10652/4525

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

11. Zawar-Reza, P. Mapping Daily Air Temperature for Antarctica Based on MODIS LST.

Degree: Geography, 2016, University of Canterbury

 Spatial predictions of near-surface air temperature (Tair) in Antarctica are required as baseline information for a variety of research disciplines. Since the network of weather… (more)

Subjects/Keywords: air temperature; Antarctica; feature selection; machine learning; MODIS LST; Field of Research::04 - Earth Sciences::0401 - Atmospheric Sciences; Field of Research::04 - Earth Sciences::0406 - Physical Geography and Environmental Geoscience

feature selection in conjunction with LOSOCV allowed removing variables that led to overfitting… …Therefore, these variables need to be checked using feature selection in conjunction with LOSOCV… …14A, 70–90. Guyon, I.; Elisseeff, A. An introduction to variable and feature selection. J… 

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APA (6th Edition):

Zawar-Reza, P. (2016). Mapping Daily Air Temperature for Antarctica Based on MODIS LST. (Thesis). University of Canterbury. Retrieved from http://hdl.handle.net/10092/13157

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):

Zawar-Reza, P. “Mapping Daily Air Temperature for Antarctica Based on MODIS LST.” 2016. Thesis, University of Canterbury. Accessed June 25, 2019. http://hdl.handle.net/10092/13157.

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

MLA Handbook (7th Edition):

Zawar-Reza, P. “Mapping Daily Air Temperature for Antarctica Based on MODIS LST.” 2016. Web. 25 Jun 2019.

Vancouver:

Zawar-Reza P. Mapping Daily Air Temperature for Antarctica Based on MODIS LST. [Internet] [Thesis]. University of Canterbury; 2016. [cited 2019 Jun 25]. Available from: http://hdl.handle.net/10092/13157.

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

Council of Science Editors:

Zawar-Reza P. Mapping Daily Air Temperature for Antarctica Based on MODIS LST. [Thesis]. University of Canterbury; 2016. Available from: http://hdl.handle.net/10092/13157

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

.