DING Weiping, WANG Jiandong, LI Yuehua, CHENG Xueyun. A Cascaded Co-evolutionary Model for Attribute Reduction and Classification Based on Coordinating Architecture with Bidirectional Elitist Optimization[J]. Chinese Journal of Electronics, 2017, 26(1): 13-21. doi: 10.1049/cje.2016.06.037
Citation: DING Weiping, WANG Jiandong, LI Yuehua, CHENG Xueyun. A Cascaded Co-evolutionary Model for Attribute Reduction and Classification Based on Coordinating Architecture with Bidirectional Elitist Optimization[J]. Chinese Journal of Electronics, 2017, 26(1): 13-21. doi: 10.1049/cje.2016.06.037

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

doi: 10.1049/cje.2016.06.037
Funds:  This work is 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), and Six Talent Peaks Project of Jiangsu Province (No.XYDXXJS-048).
  • Received Date: 2015-01-19
  • Rev Recd Date: 2015-05-30
  • Publish Date: 2017-01-10
  • 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.
  • loading
  • M. Pratama, S. Anavatti, J.E. Meng, et al., "pClass:An effective classifier for streaming examples", IEEE Transactions on Fuzzy Systems, Vol.23, No.2, pp.369-386, 2015.
    G.D. Zhao, Y. Wua, F.Q. Chen, et al., "Effective feature selection using feature vector graph for classification", Neurocomputing, Vol.151, pp.376-389, 2015.
    M.A. Lones, S.L. Smith, E.A. Jane, et al., "Evolving classifiers to recognize the movement characteristics of Parkinson's disease patients", IEEE Transactions on Evolutionary Computation, Vol.18, No.4, pp.559-576, 2014.
    Z. Pawlak, "Rough sets and intelligent data analysis", Information Sciences, Vol.147, pp.1-12, 2002.
    Y.Y. Yao, "Information granulation and rough set approximation", International Journal of Intelligent Systems, Vol.16, No.1, pp.87-104, 2001.
    A.E. Hassanien, A. Abraham, J.F. Peters, et al., "Rough sets and near sets in medical imaging:A review", IEEE Transactions on Information Technology in Biomedical, Vol.13, No.6, pp.955-68, 2009.
    J.T. Yao and N. Azam, "Web-based medical decision support systems for three-way medical decision making with gametheoretic rough sets", IEEE Transactions on Fuzzy Systems, Vol.23, No.1, pp.3-15, 2015.
    J.H. Dai and Q. Xu, "Attribute selection based on information gain ratio in fuzzy rough set theory with application to tumor classification", Applied Soft Computing, Vol.13, No.1, pp.211-221, 2013.
    Q.H. Hu, L. Zhang, S. An, et al., "On robust fuzzy rough set models", IEEE Transactions on Fuzzy Systems, Vol.20, No.4, pp.636-651, 2012.
    W.P. Ding, J.D. Wang and Z.J. Guan, "A novel minimum attribute reduction algorithm based on hierarchical elitist role model combining competitive and cooperative co-evolution", Chinese Journal of Electronics, Vol.22, No.4, pp.677-682, 2013.
    S.K.M. Wong and W. Ziarko, "On optimal decision rules in decision tables", Bulletin of Polish Academy of Science, Vol.33, No.11, pp.693-696, 1985.
    D.G. Chen and Y.Y. Yang, "Attribute reduction for heterogeneous data based on the combination of classical and fuzzy rough set models", IEEE Transactions on Fuzzy Systems, Vol.22, No.5, pp.1325-1334, 2014.
    M.Q. Ye, X.D. Wu, X.G. Hu, et al., "Knowledge reduction for decision tables with attribute value taxonomies", KnowledgeBased Systems, Vol.56, pp.68-78, 2014.
    C.Z. Wang, Q. He, D.G. Chen, et al., "A novel method for attribute reduction of covering decision", Information Sciences, Vol.254, pp.181-196, 2014.
    J. Qian, P. Lv, X.D. Yue, et al., "Hierarchical attribute reduction algorithms for big data using MapReduce", KnowledgeBased Systems, Vol.73, pp.18-31, 2015.
    W.P. Ding, Z.J. Guan, Q. Shi, et al., "A more efficient attribute self-adaptive co-evolutionary reduction algorithm by combining quantum elitist frogs and cloud model operators", Information Sciences, Vol.293, pp.214-234, 2015.
    Z.Y. Yang, K. Tang and X. Yao, "Large scale evolutionary optimization using cooperative coevolution", Information Sciences, Vol.178, No.15, pp.2985-2999, 2008.
    Y. Mei, X.D. Li and X. Yao, "Cooperative coevolution with route distance grouping for large-scale capacitated arc routing problems", IEEE Transactions on Evolutionary Computation, Vol.83, No.3, pp.435-449, 2014.
    A. Asuncion and D.J. Newman, "UCI repository of machine learning databases", http://www.ics.uci.edu/mlearn/mlrepository.html, University of California, Dept. of Information and Computer Science, Irvine, CA, 2007-6-15.
    P. Angelov and X. Zhou, "Evolving fuzzy-rule-based classifiers from data streams", IEEE Transactions on Fuzzy System, Vol.16, No.6, pp.1462-1475, 2008.
    G. Huang, D. Wang and Y. Lan, "Extreme learning machine for regression and multiclass classification", IEEE Transactions on System, Man and Cybernetics, Part B:Cybernetics, Vol.42, No.2, pp.513-529, 2012.
    C. Cortes and V. Vapnik, "Support-vector network", Machine Learning, Vol.20, No.3, pp.273-297, 1995.
    S. Suresh, K. Dong and H.J. Kim, "A sequential learning algorithm for self-adaptive resource allocation network classifier", Neurocomputing, Vol.73, pp.3012-3019, 2010.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (168) PDF downloads(570) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return