Abstract
There is an urgent need for algorithms capable of improving the selection of appropriate features that directly affect the improvement process of the algorithm’s accuracy efficiently. Therefore, in this paper, a new feature selection algorithm for binary classification is proposed, which is called binary FOX Optimization Feature Selection (BFOXFS), where BFOXFS is combined with the KNN algorithm, which is used for binary classification, and the classification error is utilized as the objective function for the proposed BFOXFS. For evaluation, BFOXFS is compared with Binary Particle Swarm Optimization Feature Selection (BPSOFS), and two versions of Binary Grey Wolf optimization algorithm for Feature Selection (BGW1FS and BGW2FS algorithms). The experimental results show the superiority of BFOXFS in feature reduction, where it chooses the minimum features with a lower fitness value comparable with others. Further, BFOXFS has better convergence capabilities, well at finding optimal values, which results in more reliable and efficient optimization algorithms. It is very suitable for implementing in real-world problems.