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 cl...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 forman 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.展开更多
基金supported in part by the Anhui Provincial Natural Science Founda-tion[1908085QG298,1908085MG232]the National Nature Science Foundation of China[91546108,61806068]+5 种基金the National Social Science Foundation of China[21BTJ002]the Anhui Provincial Science:and Technology Major Projects Grant[201903a05020020]the Fundamental Research Funds for the Central Universities[Z2019HGTA0053,JZ2019HG BZ0128]the Humanities and Social Science Fund of Ministry of Education of China[20YJA790021]the Major Project of Philosophy and Social Science Planning of Zhejiang Province[22YJRC07ZD]the Open Research Fund Program of Key Laboratory of Process Optimization and Intelligent Decision-Making(Hefei University of Technology),Ministry of Education.
文摘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 forman 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.
