Seyyedabbasi, AmirHu, GangShehadeh, Hisham A.Wang, XiaopengCanatalay, Peren Jerfi2025-04-172025-04-172025Seyyedabbasi, A., Hu, G., Shehadeh, H. A., Wang, X., & Canatalay, P. J. (2025). V-shaped and S-shaped binary artificial protozoa optimizer (APO) algorithm for wrapper feature selection on biological data. Cluster Computing, 28(3), 163.1386-78571573-7543http://dx.doi.org/10.1007/s10586-024-04927-0https://hdl.handle.net/20.500.12713/6300This study addresses the limitation of feature selection (FS) problems in high-dimensional biomedical datasets. The high-dimensional datasets contain attributes that are deemed irrelevant, redundant, and noisy. Thus, the process of feature selection is a valuable initial step aimed at improving the performance of classification models through the identification and selection of a constrained set of significant and impactful features. Due to the NP-hard nature of feature selection, it is crucial to recognize that addressing these challenges requires the utilization of metaheuristic algorithms. However, since the feature selection problem is a discrete problem, the binary version of metaheuristic algorithms should be used. To overcome these challenges, this paper proposes a novel bAPO algorithm that leverages adaptive population dynamics for more efficient exploration and exploitation of the solution space. The proposed bAPO algorithm uses V-shaped and S-shaped transfer functions to obtain wrapper feature selection in biological data. There are eight different versions of the bAPO algorithm in this study that were evaluated with 14 well-known biological datasets. The obtained results have been analyzed with the fitness value, the number of selected features, k-nearest neighbors (KNN) accuracy, support vector machine (SVM) accuracy, and random forest (RF). Statistical validation using p-value analysis demonstrates the robustness and reliability of the results. The obtained findings suggest that the proposed bAPO algorithm provides a powerful method for tackling optimization problems, particularly in high-dimensional datasets. In fitness performance, the bAPO-V1 and bAPO-V2 (27.70%) demonstrate superior performance, and in terms of reduced features, the bAPO-V2 (36.36%) algorithm achieved good performance.eninfo:eu-repo/semantics/closedAccessBinary Artificial Protozoa OptimizerBiological DataClassificationFeature SelectionOptimization ProblemsArticle283132WOS:0014015736000052-s2.0-85217282631Q110.1007/s10586-024-04927-0Q1