Genetic Programming for Simultaneous Feature Selection and Classifier Design

doi:10.1109/TSMCB.2005.854499

Genetic Programming for Simultaneous Feature Selection and Classifier Design

This paper presents an online feature selection algorithm using genetic programming (GP). The proposed GP methodology simultaneously selects a good subset of features and constructs a classifier using the selected features. For a $c$-class problem, it provides a classifier having $c$ trees. In this context, we introduce two new crossover operations to suit the feature selection process. As a byproduct, our algorithm produces a feature ranking scheme. We tested our method on several data sets having dimensions varying from 4 to 7129. We compared the performance of our method with results available in the literature and found that the proposed method produces consistently good results. To demonstrate the robustness of the scheme, we studied its effectiveness on data sets with known (synthetically added) redundant/bad features.

Publication:

IEEE Transactions on Systems, Man, and Cybernetics. Part B: Cybernetics

Pub Date:

2006

DOI:

10.1109/TSMCB.2005.854499

Bibcode:

2006ITSMB..36..106M

Keywords:

Classification;
classifier design;
evolutionary algorithm;
feature selection;
genetic programming

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Genetic Programming for Simultaneous Feature Selection and Classifier Design

Abstract