Metaheuristic-Based Hybrid Feature Selection Models

Abstract

This chapter focuses on key applications of metaheuristic techniques in the field of gene selection and classification of microarray data. The metaheuristic techniques are efficient in handling combinatorial optimization problems. In this article, two different types of metaheuristics such as Genetic algorithm (GA) and Particle Swarm Optimization (PSO) are hybridized with fuzzy-rough (FR) method for optimizing the subset selection process of microarray data. The FR method applied here deals with impreciseness and uncertainty of microarray data. The predictive accuracy of the models is evaluated by an adaptive neural net ensemble and by a rule based classifier MODLEM respectively. Moreover, the learning efficiency of the ensemble is compared with base learners and with two classical ensembles. The rule based classifier generates a set of rules for disease diagnosis and prognosis and enables to study the function of genes from gene ontology website. The experimental results of both the models prove that, hybrid metaheuristic techniques are highly effective for finding potential genes.