Binary Horse herd optimization algorithm with crossover operators for feature selection

• Published in: Computers in biology and medicine 2022-02, Vol.141, p.105152-105152, Article 105152
• Main Authors: Awadallah, Mohammed A., Hammouri, Abdelaziz I., Al-Betar, Mohammed Azmi, Braik, Malik Shehadeh, Elaziz, Mohamed Abd
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Animals; Binary horse herd optimization algorithm; Classification; Computer applications; Computing time; Crossover operators; Crossovers; Data mining; Datasets; Domains; Feature selection; Horses; Intelligence; Learning algorithms; Machine Learning; Operators (mathematics); Optimization; Optimization algorithms; Optimization techniques; Shape; Shape transfer functions; Transfer functions; Wavelet transforms
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/j.compbiomed.2021.105152

This paper proposes a binary version of Horse herd Optimization Algorithm (HOA) to tackle Feature Selection (FS) problems. This algorithm mimics the conduct of a pack of horses when they are trying to survive. To build a Binary version of HOA, or referred to as BHOA, twofold of adjustments were made: i) Three transfer functions, namely S-shape, V-shape and U-shape, are utilized to transform the continues domain into a binary one. Four configurations of each transfer function are also well studied to yield four alternatives. ii) Three crossover operators: one-point, two-point and uniform are also suggested to ensure the efficiency of the proposed method for FS domain. The performance of the proposed fifteen BHOA versions is examined using 24 real-world FS datasets. A set of six metric measures was used to evaluate the outcome of the optimization methods: accuracy, number of features selected, fitness values, sensitivity, specificity and computational time. The best-formed version of the proposed versions is BHOA with S-shape and one-point crossover. The comparative evaluation was also accomplished against 21 state-of-the-art methods. The proposed method is able to find very competitive results where some of them are the best-recorded. Due to the viability of the proposed method, it can be further considered in other areas of machine learning. •Binary Horse herd Optimization Algorithm (BHOA) is proposed for Feature selection.•Three transfer functions were investigated in BHOA: S-shape, V-shape, and U-shape.•To improve exploitation, three types of crossover operators are studied.•BHOA with S-shape transfer function using a one-point crossover is the best alternative.•Comparative evaluation against 21 methods reveals the viability of BHOA.