[1] Ding Weiping, Wang Jiandong, Shi Quan, et al. Adaptive multicascade attribute reductionbased on quantum-inspired mixed co-evolution [J]. Journal of Southeast University (English Edition), 2012, 28 (2): 145-150. [doi:10.3969/j.issn.1003-7985.2012.02.003]

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Adaptive multicascade attribute reductionbased on quantum-inspired mixed co-evolution()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

28

Issue:

2012 2

Page:

145-150

Research Field:

Computer Science and Engineering

Publishing date:

2012-06-30

Info

Title:

Adaptive multicascade attribute reductionbased on quantum-inspired mixed co-evolution

Author(s):

Ding Weiping¹;  2;  Wang Jiandong¹;  Shi Quan²;  Guan Zhijin²

¹ College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
² School of Computer Science and Technology, Nantong University, Nantong 226019, China

Keywords:

attribute reduction;  mixed co-evolution;  self-adaptive quantum rotation angle;  performance experience record;  elitist competition pool

PACS:

TP301.6

DOI:

10.3969/j.issn.1003-7985.2012.02.003

Abstract:

Due to the fact that conventional heuristic attribute reduction algorithms are poor in running efficiency and difficult in accomplishing the co-evolutionary reduction mechanism in the decision table, an adaptive multicascade attribute reduction algorithm based on quantum-inspired mixed co-evolution is proposed. First, a novel and efficient self-adaptive quantum rotation angle strategy is designed to direct the participating populations to mutual adaptive evolution and to accelerate convergence speed. Then, a multicascade model of cooperative and competitive mixed co-evolution is adopted to decompose the evolutionary attribute species into subpopulations according to their historical performance records, which can increase the diversity of subpopulations and select some elitist individuals so as to strengthen the sharing ability of their searching experience. So the global optimization reduction set can be obtained quickly. The experimental results show that, compared with the existing algorithms, the proposed algorithm can achieve a higher performance for attribute reduction, and it can be considered as a more competitive heuristic algorithm on the efficiency and accuracy of minimum attribute reduction.

References:

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Memo

Memo:

Biographies: Ding Weiping(1979—), male, graduate, lecturer, ding_wp@nuaa.edu.cn;Wang Jiandong(1945—), male, professor, aics@nuaa.edu.cn.
Foundation items: The National Natural Science Foundation of China(No.61139002, 61171132), the Funding of Jiangsu Innovation Program for Graduate Education(No.CXZZ11_0219), the Natural Science Foundation of Jiangsu Province(No.BK2010280), the Open Project of Jiangsu Provincial Key Laboratory of Computer Information Processing Technology(No.KJS1023), the Applying Study Foundation of Nantong(No.BK2011062).
Citation: Ding Weiping, Wang Jiandong, Shi Quan, et al.Adaptive multicascade attribute reduction based on quantum-inspired mixed co-evolution[J].Journal of Southeast University(English Edition), 2012, 28(2):145-150.[doi:10.3969/j.issn.1003-7985.2012.02.003]

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