Ensemble Classifier Design Based on Perturbation Binary Salp Swarm Algorithm for Classification

• Published in: Computer modeling in engineering & sciences Vol. 135; no. 1; pp. 653 - 671
• Main Authors: Zhu, Xuhui, Xia, Pingfan, He, Qizhi, Ni, Zhiwei, Ni, Liping
• Format: Journal Article
• Language: English
• Published: Henderson Tech Science Press 2023
• Subjects: Algorithms; Classification; Ensemble learning; Machine learning; Optimization; Perturbation
• ISSN: 1526-1506; 1526-1492; 1526-1506
• DOI: 10.32604/cmes.2022.022985

Multiple classifier system exhibits strong classification capacity compared with single classifiers, but they require significant computational resources. Selective ensemble system aims to attain equivalent or better classification accuracy with fewer classifiers. However, current methods fail to identify precise solutions for constructing an ensemble classifier. In this study, we propose an ensemble classifier design technique based on the perturbation binary salp swarm algorithm (ECDPB). Considering that extreme learning machines (ELMs) have rapid learning rates and good generalization ability, they can serve as the basic classifier for creating multiple candidates while using fewer computational resources. Meanwhile, we introduce a combined diversity measure by taking the complementarity and accuracy of ELMs into account; it is used to identify the ELMs that have good diversity and low error. In addition, we propose an ECDPB with powerful optimizing ability; it is employed to find the optimal subset of ELMs. The selected ELMs can then be used to form an ensemble classifier. Experiments on 10 benchmark datasets have been conducted, and the results demonstrate that the proposed ECDPB delivers superior classification capacity when compared with alternative methods.