Cooperative extended rough attribute reduction algorithm based on improved PSO

查看全文 作　　者：Weiping [1,2]Ding;Jiandong [1]Wang;Zhijin [2]Guan

机构地区：[1]College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, E R. China;[2]School of Computer Science and Technology, Nantong University, Nantong 226019, E R. China

出　　处：《Journal of Systems Engineering and Electronics》索引2012年第23卷第1期,共7页

基　　金：supported by the National Natural Science Foundation of China (60873069; 61171132);the Jiangsu Government Scholarship for Overseas Studies (JS-2010-K005);the Funding of Jiangsu Innovation Program for Graduate Education (CXZZ11 0219);the Open Project Program of Jiangsu Provincial Key Laboratory of Computer Information Processing Technology (KJS1023);the Applying Study Foundation of Nantong (BK2011062) 摘　　要：Particle swarm optimization (PSO) is a new heuristic algorithm which has been applied to many optimization problems successfully. Attribute reduction is a key studying point of the rough set theory, and it has been proven that computing minimal reduction of decision tables is a non-derterministic polynomial (NP)-hard problem. A new cooperative extended attribute reduction algorithm named Co-PSAR based on improved PSO is proposed, in which the cooperative evolutionary strategy with suitable fitness functions is involved to learn a good hypothesis for accelerating the optimization of searching minimal attribute reduction. Experiments on Benchmark functions and University of California, Irvine (UCI) data sets, compared with other algorithms, verify the superiority of the Co-PSAR algorithm in terms of the convergence speed, efficiency and accuracy for the attribute reduction. 关键词：属性约简算法粗糙集理论 PSO 合作粒子群优化 PSAR 启发式算法优化问题

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Cooperative extended rough attribute reduction algorithm based on improved PSO

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