Binary Horse herd optimization algorithm with crossover operators for feature selection. (February 2022) - Awadallah, Mohammed A.; Hammouri, Abdelaziz I.; Al-Betar, Mohammed Azmi; Braik, Malik Shehadeh; Elaziz, Mohamed Abd
Abstract: 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. Highlights: 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.