Simultaneous Feature Selection and Feature Extraction for Pattern Classification.

Date of Submission

December 2009

Date of Award

Winter 12-12-2010

Institute Name (Publisher)

Indian Statistical Institute

Document Type

Master's Dissertation

Degree Name

Master of Technology

Subject Name

Computer Science


Machine Intelligence Unit (MIU-Kolkata)


Murthy, C. A. (MIU-Kolkata; ISI)

Abstract (Summary of the Work)

Feature subset selection and extraction algorithms are actively and extensively studied in machine learning literature to reduce the high dimensionality of feature space, since high dimensional data sets are generally not efficiently and effectively handled by machine learning and pattern recognition algorithms. In this thesis, a novel approach to combining feature selection and feature extraction algorithms. We present an algorithm which incorporates both. The performance of the algorithm is established over real-life data sets of different sizes and dimensions.


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Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.


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