An enhanced binary Rat Swarm Optimizer based on local-best concepts of PSO and collaborative crossover operators for feature selection. (August 2022)

• Record Type: Journal Article
• Authors: Awadallah, Mohammed A.; Al-Betar, Mohammed Azmi; Braik, Malik Shehadeh; Hammouri, Abdelaziz I.; Doush, Iyad Abu; Zitar, Raed Abu
• Is Part Of: Computers in biology and medicine. Volume 147(2022)
• Publication Date: 2022-08
• Subjects: Feature selection -- Rat swarm optimizer -- Particle swarm optimization -- S-shape transfer function -- Optimization; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2022.105675
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: In this paper, an enhanced binary version of the Rat Swarm Optimizer (RSO) is proposed to deal with Feature Selection (FS) problems. FS is an important data reduction step in data mining which finds the most representative features from the entire data. Many FS-based swarm intelligence algorithms have been used to tackle FS. However, the door is still open for further investigations since no FS method gives cutting-edge results for all cases. In this paper, a recent swarm intelligence metaheuristic method called RSO which is inspired by the social and hunting behavior of a group of rats is enhanced and explored for FS problems. The binary enhanced RSO is built based on three successive modifications: i) an S-shape transfer function is used to develop binary RSO algorithms; ii) the local search paradigm of particle swarm optimization is used with the iterative loop of RSO to boost its local exploitation; iii) three crossover mechanisms are used and controlled by a switch probability to improve the diversity. Based on these enhancements, three versions of RSO are produced, referred to as Binary RSO (BRSO), Binary Enhanced RSO (BERSO), and Binary Enhanced RSO with Crossover operators (BERSOC). To assess the performance of these versions, a benchmark of 24 datasets from various domains is used. The proposed methods are assessed concerning the fitness value, number of selected features, classification accuracy, specificity, sensitivity, and computational time. The best performance is achieved by BERSOC followed by BERSO and then BRSO. These proposed versions are comparatively assessed against 25 well-regarded metaheuristic methods and five filter-based approaches. The obtained results underline their superiority by producing new best results for some datasets. Highlights: An enhanced binary version of the Rat Swarm Optimizer (RSO) is proposed to deal with Feature Selection. S-shape transfer function is utilized to transfer from continuous RSO to the binary RSO. The concept of the local-best solutions from PSO has been utilized with RSO to enhance its local exploitation. To improve diversity, three types of crossover operators are studied. Comparative evaluation against 25 optimization methods and 5 filter-based approaches reveals the viability of the proposed algorithm.