Improved filter ranking incorporated binary black hole algorithm for feature selection

Pure wrapper-based feature selection methods like Black Hole are computationally effective but ignore the statistical properties of individual features. This paper embeds filter-based rankings — Pearson correlation combined with Gini importance, and Pearson correlation combined with mutual information — directly into the Black Hole algorithm’s fitness function, switching between the filter-guided and standard criteria probabilistically during the search. The hybrid strategy produces smaller feature subsets with higher accuracy than the standalone Black Hole algorithm, and performs competitively against a filter-enhanced Ant Colony Optimization baseline across diverse benchmark datasets from science and engineering.

Recommended citation: Ovhal, P., Kulkarni, S. & Valadi, J.K. Improved Filter Ranking Incorporated Binary Black Hole Algorithm for Feature Selection. SN COMPUT. SCI. 3, 51 (2022). https://doi.org/10.1007/s42979-021-00933-w https://link.springer.com/article/10.1007/s42979-021-00933-w