Learning Bayesian networks to perform feature selection

Bayesian networks have been widely applied to the feature selection problem. The existing approaches learn a Bayesian network from the available dataset and, afterward, utilize the Markov Blanket of the target feature as the criterion to select the relevant features. The Bayesian network learning can be viewed as a search and optimization procedure, where a search mechanism explores the space of all network structures while a scoring metric evaluates each candidate solution based on the likelihood. This paper investigates the application of an immune-inspired algorithm as the search procedure for obtaining high-quality Bayesian networks, motivated by the dynamical control of the population size and diversity along the search. Due to the resulting multimodal search capability, in a single run of the algorithm several subsets of features are obtained. Experiments on ten datasets were carried out in order to evaluate the proposed methodology in classification problems, and reduced-size subsets of features were produced.