Multi‐objective feature selection using a Bayesian artificial immune system

The purpose of this paper is to apply a multi‐objective Bayesian artificial immune system (MOBAIS) to feature selection in classification problems aiming at minimizing both the classification error and cardinality of the subset of features. The algorithm is able to perform a multimodal search maintaining population diversity and controlling automatically the population size according to the problem. In addition, it is capable of identifying and preserving building blocks (partial components of the whole solution) effectively.

The algorithm evolves candidate subsets of features by replacing the traditional mutation operator in immune‐inspired algorithms with a probabilistic model which represents the probability distribution of the promising solutions found so far. Then, the probabilistic model is used to generate new individuals. A Bayesian network is adopted as the probabilistic model due to its capability of capturing expressive interactions among the variables of the problem. In order to evaluate the proposal, it was applied to ten datasets and the results compared with those generated by state‐of‐the‐art algorithms.

The experiments demonstrate the effectiveness of the multi‐objective approach to feature selection. The algorithm found parsimonious subsets of features and the classifiers produced a significant improvement in the accuracy. In addition, the maintenance of building blocks avoids the disruption of partial solutions, leading to a quick convergence.

The originality of this paper relies on the proposal of a novel algorithm to multi‐objective feature selection.