A Cascaded Co-evolutionary Model for Attribute Reduction and Classification Based on Coordinating Architecture with Bidirectional Elitist Optimization  

作　　者：DING Weiping WANG Jiandong LI Yuehua CHENG Xueyun 

机构地区：[1]School of Computer Science and Technology, Nantong University [2]Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University [3]Provincial Key Laboratory for Computer Information Processing Technology, Soochow University [4]College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics

出　　处：《Chinese Journal of Electronics》2017年第1期13-21,共9页电子学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(No.61300167,No.61139002);Natural Science Foundation of Jiangsu Province(BK20151274);Sponsored by Qing Lan Project of Jiangsu Province,the Open Project Program of Jiangsu Provincial Key Laboratory of Computer Information Processing Technology(No.KJS1517);Six Talent Peaks Project of Jiangsu Province(No.XYDXXJS-048)

摘　　要：A cascaded co-evolutionary model for Attribute reduction and classification based on Coordinating architecture with bidirectional elitist optimization(ARC-CABEO) is proposed for the more practical applications. The regrouping and merging coordinating strategy of ordinary-elitist-role-based population is introduced to represent a more holistic cooperative co-evolutionary framework of different populations for attribute reduction. The master-slave-elitist-based subpopulations are constructed to coordinate the behaviors of different elitists, and meanwhile the elitist optimization vector with the strongest balancing between exploration and exploitation is selected out to expedite the bidirectional attribute co-evolutionary reduction process. In addition, two coupled coordinating architectures and the elitist optimization vector are tightly cascaded to perform the co-evolutionary classification of reduction subsets. Hence the preferring classification optimization goal can be achieved better. Some experimental results verify that the proposed ARC-CABEO model has the better feasibility and more superior classification accuracy on different UCI datasets, compared with representative algorithms.A cascaded co-evolutionary model for Attribute reduction and classification based on Coordinating architecture with bidirectional elitist optimization（ARC-CABEO） is proposed for the more practical applications. The regrouping and merging coordinating strategy of ordinary-elitist-role-based population is introduced to represent a more holistic cooperative co-evolutionary framework of different populations for attribute reduction. The master-slave-elitist-based subpopulations are constructed to coordinate the behaviors of different elitists, and meanwhile the elitist optimization vector with the strongest balancing between exploration and exploitation is selected out to expedite the bidirectional attribute co-evolutionary reduction process. In addition, two coupled coordinating architectures and the elitist optimization vector are tightly cascaded to perform the co-evolutionary classification of reduction subsets. Hence the preferring classification optimization goal can be achieved better. Some experimental results verify that the proposed ARC-CABEO model has the better feasibility and more superior classification accuracy on different UCI datasets, compared with representative algorithms.

关 键 词：Attribute reduction for classification Bidirectional elitist optimization Coordinating architecture Cascaded co-evolutionary model 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TS255.1[自动化与计算机技术—控制科学与工程]