Hybridization of evolutionary mechanisms for feature subset selection in unsupervised learning

Abstract

Feature subset selection for unsupervised learning, is a very important topic in artificial intelligence because it is the base for saving computational resources. In this implementation we use a typical testor's methodology in order to incorporate an importance index for each variable. This paper presents the general framework and the way two hybridized meta-heuristics work in this NP-complete problem. The evolutionary mechanisms are based on the Univariate Marginal Distribution Algorithm (UMDA) and the Genetic Algorithm (GA). GA and UMDA - Estimation of Distribution Algorithm (EDA) use a very useful rapid operator implemented for finding typical testors on a very large dataset and also, both algorithms, have a local search mechanism for improving time and fitness. Experiments show that EDA is faster than GA because it has a better exploitation performance; nevertheless, GA' solutions are more consistent. © 2009 Springer-Verlag Berlin Heidelberg.