A Multi-objective Binary Differential Evolution Operator for Feature Selection

Feature selection is a pivotal component of machine learning and data analysis, to optimize model performance by eliminating irrelevant and redundant features, to address the challenges associated with the “curse of dimensionality” and interpretability. In this context, we present feature selection as a multi-objective binary optimization task with the dual aim of maximizing classification accuracy while minimizing the number of selected features. In order to address this optimization challenge, we introduce the Multi-objective Binary Differential Evolution algorithm (MOBDE). It's worth noting that DE originally is an extremely powerful real-value coded algorithm, and to make it binary, set-based operators must replace vector-based operations. Optimization in binary space is deemed more suitable than real-value optimization because a many-to-one mapping can waste the efforts of the optimizer when solving a binary problem like feature selection. The algorithm leverages a partial opposition-based binary operator to generate diverse solutions to enhance its exploration within the search space. Additionally, it incorporates a majority voting mechanism as a local search strategy to bolster the algorithm's exploitation capabilities. Results from experimentation on eleven datasets underscore the efficiency of MOBDE, outperforming the widely recognized NSGA-II method in terms of the hypervolume (HV) performance metric and minimizing the number of selected features. The proposed algorithm and its experimental outcomes are comprehensively detailed and analyzed, offering valuable insights into its efficacy for feature selection tasks.