Attribute Reduction Method Based on Improved Binary Glowworm Swarm Optimization Algorithm and Neighborhood Rough Set

被引：0
|
作者：
Peng, Peng [1 ,2 ,3 ]
Ni, Zhiwei [1 ,3 ]
Zhu, Xuhui [1 ,3 ]
Xia, Pingfan [1 ,3 ]
机构：
[1] School of Management, Hefei University of Technology, Hefei,230009, China
[2] North Minzu University, Yinchuan,750021, China
[3] Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, Hefei University of Technology, Hefei,230009, China
来源：
Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2020年 / 33卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Learning algorithms - Rough set theory - Swarm intelligence;
D O I：
10.16451/j.cnki.issn1003-6059.202002001
中图分类号：
学科分类号：
摘要：
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. © 2020, Science Press. All right reserved.
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页码：95 / 105
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