Paper Info
Title
Minimum Attribute Reduction Algorithm Based On Quick Extraction And Multi-Strategy Social Spider Optimization
Abstract
Intelligent optimization algorithm combined with rough set theory to solve minimum attribute reduction (MAR) is time consuming due to repeated evaluations of the same position. The algorithm also finds in poor solution quality because individuals are not fully explored in space. This study proposed an algorithm based on quick extraction and multi-strategy social spider optimization (QSSOAR). First, a similarity constraint strategy was called to constrain the initial state of the population. In the iterative process, an adaptive opposition-based learning (AOBL) was used to enlarge the search space. To obtain a reduction with fewer attributes, the dynamic redundancy detection (DRD) strategy was applied to remove redundant attributes in the reduction result. Furthermore, the quick extraction strategy was introduced to avoid multiple repeated computations in this paper. By combining an array with key-value pairs, the corresponding value can be obtained by simple comparison. The proposed algorithm and four representative algorithms were compared on nine UCI datasets. The results show that the proposed algorithm performs well in reduction ability, running time, and convergence speed. Meanwhile, the results confirm the superiority of the algorithm in solving MAR.
Year
DOI
Venue
2021
10.3233/JIFS-210133
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
Keywords
DocType
Volume
Intelligent optimization, rough set theory, attribute reduction, social spider optimization, opposition-based learning
Journal
40
Issue
ISSN
Citations 
6
1064-1246
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Qianjin Wei100.34
Chengxian Wang200.34
Yimin Wen331.38