Feature Subset Selection by Means of a Bayesian Artificial Immune System

This paper proposes the application of a novel bio-inspired algorithm as a search engine to the feature subset selection problem. We may interpret our algorithm as an estimation of distribution algorithm that adopts an artificial immune system to implement the search process in the space of all features and a Bayesian network to implement the probabilistic model of the promising solutions. The characteristics of the proposed algorithm are the capability of effectively identifying and manipulating building blocks, maintenance of diversity in the population, and automatic control of the population size. These properties allow the algorithm to perform a multimodal search, known to be of great relevance in feature selection problems. Experiments on five datasets were carried out in order to evaluate the proposed methodology in classification problems and its performance compares favorably to that produced by contenders.