A novel binary Stellar Oscillation Optimizer for feature selection optimization problems

Abstract

Stellar Oscillation Optimizer (SOO) takes its core inspiration from the study of stellar pulsations, a domain often referred to as asteroseismology which is formulated as an optimization algorithm for continuous domain. In this paper, the Binary version of Stellar Oscillation Optimizer (BSOO) is proposed for Feature Selection (FS) problems. BSOO introduces binary adaptations, including threshold-based encoding, controlled oscillatory movements, and a top-solution influence mechanism. In order to evaluate the BSOO, sixteen FS datasets are used with different numbers of features, samples, and class labels. Seven performance measures are also used, which are: fitness value, number of selected features, accuracy, sensitivity, specificity, Precision, and F-measure. An intensive comparative evaluation against 18 state-of-the-art optimization algorithms using the same datasets has been conducted. The results show that the proposed BSOO version is able to compete well with the other FS-based methods where it is able to overcome several methods and produce the best overall results for some datasets on different measurements. Furthermore, the convergence behavior to show the optimization behavior of BSOO during the search is investigated and visualized. Interestingly, the BSOO is able to provide a suitable trade-off between the global wide-range exploration and local nearby exploitation during the optimization process. This is proved using the statistical Wilcoxon Rank-Sum Test Results. In conclusion, this paper provides a new alternative solution for FS research community that is able to work well for many FS instances and find the optimal solution. The source code of BSOO is publicly available for both MATLAB at: https://www.mathworks.com/matlabcentral/fileexchange/180096-bsoo-binary-stellar-oscillation-optimizer and PYTHON at: https://github.com/AliRodan/BSOO-Binary-Stellar-Oscillation-Optimizer.