Investigating the Performance of a Novel Modified Binary Black Hole Optimization Algorithm for Enhancing Feature Selection

Abstract

High-dimensional datasets often harbor redundant, irrelevant, and noisy features that detrimentally impact classification algorithm performance. Feature selection (FS) aims to mitigate this issue by identifying and retaining only the most pertinent features, thus reducing dataset dimensions. In this study, we propose an FS approach based on black hole algorithms (BHOs) augmented with a mutation technique termed MBHO. BHO typically comprises two primary phases. During the exploration phase, a set of stars is iteratively modified based on existing solutions, with the best star selected as the “black hole”. In the exploration phase, stars nearing the event horizon are replaced, preventing the algorithm from being trapped in local optima. To address the potential randomness-induced challenges, we introduce inversion mutation. Moreover, we enhance a widely used objective function for wrapper feature selection by integrating two new terms based on the correlation among selected features and between features and classification labels. Additionally, we employ a transfer function, the V2 transfer function, to convert continuous values into discrete ones, thereby enhancing the search process. Our approach undergoes rigorous evaluation experiments using fourteen benchmark datasets, and it is compared favorably against Binary Cuckoo Search (BCS), Mutual Information Maximization (MIM), Joint Mutual Information (JMI), and minimum Redundancy Maximum Eelevance (mRMR), approaches. The results demonstrate the efficacy of our proposed model in selecting superior features that enhance classifier performance metrics. Thus, MBHO is presented as a viable alternative to the existing state-of-the-art approaches. We make our implementation source code available for community use and further development.