A multi-objective artificial butterfly optimization approach for feature selection

Abstract

Feature selection plays an essential role in machine learning since high dimensional real-world datasets are becoming more popular nowadays. The very basic idea consists in selecting a compact but representative set of features that reduce the computational cost and minimize the classification error. In this paper, the authors propose single, multi- and many-objective binary versions of the Artificial Butterfly Optimization (ABO) in the context of feature selection. The authors also propose two different approaches: (i) the first one (MO-I) aims at optimizing the classification accuracy of each class individually, while (ii) the second one (MO-II) considers the feature set minimization in the process either. The experiments were conducted over eight public datasets, and the proposed approaches are compared against the well-known Particle Swarm Optimization, Firefly Algorithm, Flower Pollination Algorithm, Brainstorm Optimization, and the Black Hole Algorithm. The results showed that the binary single-objective ABO performed better than the other meta-heuristic techniques, selecting fewer features and also figuring a lower computational burden. Concerning multi- and many-objective feature selection, both MO-I and MO-II approaches performed better than their single-objective meta-heuristic counterparts.