language: en

1 FEATURE SUBSET SELECTION USING A GENETIC ALGORITHM

Jihoon Yang and Vasant Honavar
Artificial Intelligence Research Group Department of Computer Science
226 Atanasoff Hall
Iowa State University
Ames, IA 50011 U.S.A.
{yang|honavar}@cs.iastate.edu

Abstract: Practical pattern classification and knowledge discovery problems require selection of a subset of attributes or features (from a much larger set) to represent the patterns to be classified. This is due to the fact that the performance of the classifier (usually induced by some learning algorithm) and the cost of classification are sensitive to the choice of the features used to construct the classifier. Exhaustive evaluation of possible feature subsets is usually infeasible in practice because of the large amount of computational effort required. Genetic algorithms, which belong to a class of randomized heuristic search techniques, offer an attractive approach to find near-optimal solutions to such optimization problems. This paper presents an approach to feature subset selection using a genetic algorithm. Some advantages of this approach include the ability to accommodate multiple criteria such as accuracy and cost of classification into the feature selection process and to find feature subsets that perform well for particular choices of the inductive learning algorithm used to construct the pattern classifier. Our experiments with several benchmark real-world pattern classification problems demonstrate the feasibility of this approach to feature subset selection in the automated design of neural networks for pattern classification and knowledge discovery.

1.1 INTRODUCTION