Attribute Reduction Method Based on Improved Binary Glowworm Swarm Optimization Algorithm and Neighborhood Rough Set
PENG Peng1,2,3, NI Zhiwei1,3, ZHU Xuhui1,3, XIA Pingfan1,3
1. School of Management, Hefei University of Technology, Hefei 230009; 2. North Minzu University, Yinchuan 750021; 3. Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, Hefei University of Technology, Hefei 230009
Abstract Aiming at the problems of dimension reduction and redundancy removing, an attribute reduction method based on improved binary glowworm swarm optimization algorithm and neighborhood rough set is proposed. Firstly, the population is collaborative initialization using reverse learning, and the mapping of the change function based on Sigmoid is employed for binary coding, and an improved binary glowworm opti-mization algorithm is proposed with Lévy flight position update strategy. Secondly, neighborhood rough set is employed as an evaluation criterion, and the proposed algorithm is utilized as an search strategy for attribute reduction. Finally, experiments on the standard UCI datasets demonstrate the effectiveness of the attribute reduction method, and the better convergence speed and accuracy of the proposed algorithm is verified.
Fund:Supported by National Natural Science Foundation of China(No.71490725,71521001,91546108), Youth Program of National Natural Science Foundation of China(No.71701061), Natural Science Foundation of Anhui Province(No.1908085QG298), Special Fund Project of Basic Scientific Research Business Cost of Central University(No.JZ2019HGTA0053,JZ2019HGBZ0128)
Corresponding Authors:
NI Zhiwei, Ph.D., professor. His research interests include artificial intelligence, machine learning and cloud computing.
About author:: PENG Peng, Ph.D.candidate,lecturer. His research interests include data mining and intelligent optimization; ZHU Xuhui, Ph.D., lecturer. His research interests include intelligent computing and machine learning; XIA Pingfan, Ph.D. candidate. Her research interests include intelligent computing, machine learning and internet finance.
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2020, Vol. 33 
