Construction of classifier with feature selection based on genetic programming

Abstract

This paper presents a genetic programming (GP) based approach for designing classifiers with feature selection using a modified crossover operator. 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. To overcome the difficulties with standard crossover operator, we have used a crossover operator which discovers the best possible crossover site for a subtree and attains higher fitness values while processing fewer individuals. We have tested our method on several datasets having large number of features. We have compared the performance of our method with results available in the literature and found that the proposed method generates good results.