Feature Subset Selection, Class Separability, and Genetic Algorithms

Abstract. The performance of classification algorithms in machine learning is affected by the features used to describe the labeled examples presented to the inducers. Therefore, the problem of feature subset selection has received considerable attention. Genetic approaches to this problem usually follow the wrapper approach: treat the inducer as a black box that is used to evaluate candidate feature subsets. The evaluations might take a considerable time and the traditional approach might be impractical for large data sets. This paper describes a hybrid of a simple genetic algorithm and a method based on class separability applied to the selection of feature subsets for classification problems. The proposed hybrid was compared against each of its components and two other feature selection wrappers that are used widely. The objective of this paper is to determine if the proposed hybrid presents advantages over the other methods in terms of accuracy or speed in this problem. The expe...

• Type: Conference
• Year: 2004
• Where: GECCO
• Authors: Erick Cantú-Paz
• Keywords: Candidate Feature Subsets, Feature Subset Selection, Feature Subsets, GECCO 2004