Effective performance enhancement and feature reduction can be achieved by feature selection, which is the procedure of evaluating and choosing the most informative features. Consequently, this paper proposes a Binary Border Collie Optimization (BBCO) to address the feature selection problem in classification tasks. The sigmoidal function is used in the proposed algorithm to compress the continually updated position in order to achieve BBCO. Therefore, the proposed algorithm is utilized to determine the ideal feature subset from the initial feature set. To assess the performance of the proposed algorithm, BBCO is compared with Binary Firefly Algorithm (BFA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Binary Gray Wolf Optimization (BGWO). The experiments on eighteen datasets collected from University of California Irvine (UCI) machine learning data repository results show the superiority of BBCO in 15 datasets, which means 83.3% in terms of classification accuracy with a reduced features number being chosen. Furthermore, BBCO has a very low average selected feature ratio, it is more beneficial for applications in the actual world.

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