Feature Selection Methods: Genetic Algorithms vs. Greedy-like Search

Abstract

. This paper presents a comparison between two feature selection methods, the Importance Score (IS) which is based on a greedy-like search and a genetic algorithm-based (GA) method, in order to better understand their strengths and limitations and their area of application. The results of our experiments show a very strong relation between the nature of the data and the behavior of both systems. The Importance Score method is more efficient when dealing with little noise and small number of interacting features, while the genetic algorithms can provide a more robust solution at the expense of increased computational effort. Keywords. feature selection, machine learning, genetic algorithms, search. 1. INTRODUCTION Feature selection is a problem that has to be addressed in many areas, especially in artificial intelligence. The main issues in developing feature selection techniques are choosing a small feature set in order to reduce the cost and running time of a given system, as well as...