Improving binary crow search algorithm for feature selection

• Published in: Journal of intelligent systems Vol. 32; no. 1; pp. 1598 - 610
• Main Authors: Hamed Alnaish, Zakaria A., Algamal, Zakariya Yahya
• Format: Journal Article
• Language: English
• Published: Berlin De Gruyter 16-02-2023; Walter de Gruyter GmbH
• Subjects: Accuracy; Antiinfectives and antibacterials; binary crow search algorithm; Classification; Computational efficiency; Computing time; Datasets; Feature selection; Glycoproteins; Performance evaluation; quasi-oppositional method; Search algorithms
• ISSN: 2191-026X; 0334-1860; 2191-026X
• DOI: 10.1515/jisys-2022-0228

The feature selection (FS) process has an essential effect in solving many problems such as prediction, regression, and classification to get the optimal solution. For solving classification problems, selecting the most relevant features of a dataset leads to better classification accuracy with low training time. In this work, a hybrid binary crow search algorithm (BCSA) based quasi-oppositional (QO) method is proposed as an FS method based on wrapper mode to solve a classification problem. The QO method was employed in tuning the value of flight length in the BCSA which is controlling the ability of the crows to find the optimal solution. To evaluate the performance of the proposed method, four benchmark datasets have been used which are human intestinal absorption, HDAC8 inhibitory activity (IC50), P-glycoproteins, and antimicrobial. Accordingly, the experimental results are discussed and compared against other standard algorithms based on the accuracy rate, the average number of selected features, and running time. The results have proven the robustness of the proposed method relied on the high obtained value of accuracy (84.93–95.92%),
-mean (0.853–0.971%), and average selected features (4.36–11.8) with a relatively low computational time. Moreover, to investigate the effectiveness of the proposed method, Friedman test was used which declared that the performance supremacy of the proposed BCSA-QO with four datasets was very evident against BCSA and CSA by selecting the minimum relevant features and producing the highest accuracy classification rate. The obtained results verify the usefulness of the proposed method (BCSA-QO) in the FS with classification in terms of high classification accuracy, a small number of selected features, and low computational time.