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.
Electronics and Communication Sciences Unit, Indian Statistical Institute, Calcutta-700108, India. muni_r@isical.ac.in
Durga Prasad
DP
Muni
Nikhil R
NR
Pal
Jyotirmoy
J
Das
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003661:Decision Support Techniques; D016247:Information Storage and Retrieval / Q000379:methods*; D008957:Models, Genetic; D010363:Pattern Recognition, Automated / Q000379:methods*