Improved binary differential evolution with dimensionality reduction mechanism and binary stochastic search for feature selection

Computer systems store massive amounts of data with numerous features, leading to the need to extract the most important features for better classification in a wide variety of applications. Poor performance of various machine learning algorithms may be caused by unimportant features that increase the time and memory required to build a classifier. Feature selection (FS) is one of the efficient approaches to reducing the unimportant features. This paper, therefore, presents a new FS, named BDE-BSS-DR, that utilizes Binary Differential Evolution (BDE), Binary Stochastic Search (BSS) algorithm, and Dimensionality Reduction (DR) mechanism. The BSS algorithm increases the search capability of the BDE by escaping from local optimal points and exploring the search space. The DR mechanism then reduces the dimensions of the search space gradually. As a result of using DR, the local optima of the search space and the problem of wrong removal of important features before starting the search process are reduced. The algorithm's efficiency is evaluated on 20 different medical datasets. The obtained outcomes indicate that the BDE-BSS-DR outperforms the BDE and BDE-BSS algorithms significantly. Furthermore, the effectiveness of the proposed algorithms in selecting the most important features of the heart disease data, several cancer diseases, and COVID-19 are also compared with several other state-of-the-art methods. Our results show that the BDE-BSS-DR with SVM classifier has a significant advantage over other methods with an average classification accuracy of 95.05% in heart disease and 99.40% in COVID-19 disease. In addition, the comparisons made with KNN and SVM classification prove the efficiency of the DR and BSS in generating a subset of optimal and informative features.