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

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