JIANG Yu. Minimal Element Selection in the Discernibility Matrix for Attribute Reduction[J]. Chinese Journal of Electronics, 2019, 28(1): 6-12. doi: 10.1049/cje.2018.06.018
Citation: JIANG Yu. Minimal Element Selection in the Discernibility Matrix for Attribute Reduction[J]. Chinese Journal of Electronics, 2019, 28(1): 6-12. doi: 10.1049/cje.2018.06.018

Minimal Element Selection in the Discernibility Matrix for Attribute Reduction

doi: 10.1049/cje.2018.06.018
Funds:  This work is supported by the Young and Middle-aged Academic Leader Foundation of Chengdu University of Information Technology (No.J201609).
  • Received Date: 2016-07-08
  • Rev Recd Date: 2016-12-01
  • Publish Date: 2019-01-10
  • Discernibility matrix is a beautiful theoretical result to get reducts in the rough set, but the existing algorithms based on discernibility matrix share the same problem of heavy computing load and large store space, since there are numerous redundancy elements in discernibility matrix and these algorithms employ all elements to find reducts. We introduce a new method to compute attribute significance. A novel approach is proposed, called minimal element selection tree, which utilizes many strategies to eliminate redundancy elements in discernibility matrix. This paper presents two methods to find out a minimal reduct for a given decision table based this tree structure. The experimental results with UCI data show that the proposed approaches are effective and efficient than the benchmark methods.
  • loading
  • Pawlak Z., "Rough sets", International Journal of Computer and Information Science, Vol.11, No.5, pp.341-356, 1982.
    Slowinski R., "Intelligent decision support-handbook of applications and advances of the rough sets theory", London:Kluwer Academic Publishers, 1992.
    K. Thangavel and A. Pethalakshmi,"Dimensionality reduction based on rough set theory:A review", Applied Soft Computing, Vol.9, No.1, pp.1-12, 2009.
    Zhang Q., Shen W., "Research on attribute reduction algorithm with weights", J. Intell. Fuzzy Syst., Vol.27, No.2, pp.1011-1019, 2014.
    YE Dongyi, CHENG Zhaojiong, "An efficient combinatorial artificial bee colony algorithm for solving minimum attribute reduction problem", Journal of Electronics, Vol.43, No.5, pp.1014-1020, 2015.
    Majdi Mafarja and Derar Eleyan, "Ant colony optimization based feature selection in rough set theory", International Journal of Computer Science and Electronics Engineering, Vol.1, No.2, pp.244-247, 2013.
    LU Zhengcai, et al., "Constructing rough set based unbalanced binary tree for feature selection", Chinese Journal of Electronics, Vol.23, No.3, pp.474-479, 2014.
    A. Skowron and C. Rauszer, "The discernibility matrices and functions in information systems", Intelligent Decision Support-Handbook of Applications and Advances of the Rough Sets Theory, R. Slowinski, ed., Kluwer Academic Publishers, pp.331-362, 1992.
    Yao, Y.Y. and Zhao, Y. "Discernibility matrix simplification for constructing attribute reducts", Information Sciences, Vol.179, No.5, pp.867-882, 2009.
    Jiang Yu, "Minimal attribute reduction for rough set based on attribute enumeration tree", International Journal of Advancements in Computing Technology, Vol.4, No.19, pp.391-399, 2012.
    Jiang Yu, et al., "Attribute reduction algorithm of rough sets based on discernibility matrix", Journal of System Simulation, Vol.20, No.14, pp.3717-3720,3725, 2008.
    Yang M. and Yang P., "A novel condensing tree structure for rough set feature selection", Neurocomputing, Vol.71, No.4, pp.1092-1100, 2008.
    Yang M. and Yang P., "A novel approach to improving C-Tree for feature selection", Applied Soft Computing, Vol.11, No.2, pp.1924-1931, 2011.
    JIANG Yu, "Attribute reduction with rough set based on discernibility information tree", Control and Decision, Vol.30, No.8, pp.1531-1536, 2015.
    Yu Jiang and Yang Yu, "Minimal attribute reduction with rough set based on compactness discernibility information tree", Soft Computing, Vol.20, No.6, pp.2233-2243, 2016.
    Y.H. Qian, et al., "Positive approximation:An accelerator for attribute reduction in rough set theory", Artificial Intelligence, Vol.174, pp.597-618, 2010.
    JIANG Yu, et al., "Fast algorithm for computing attribute reduction based on bucket sort", Control and Decision, Vol.26, No.2, pp.207-212, 2011.
    GE Hao, et al., "An efficient attribute reduction algorithm based on conflict region", Chinese Journal of Computers, Vol.35, No.2, pp.342-350, 2012.
    SiYuan Jing, "A hybrid genetic algorithm for feature subset selection in rough set theory", Soft Computing, Vol.18, No.7, pp.1373-1382, 2014.
    Q.H. Hu, et al., "Hybrid attribute reduction based on a novel fuzzy-rough model and information granulation", Pattern Recognition, Vol.40, pp.3509-3521, 2007.
    Q.H. Hu, et al., "Information-preserving hybrid data reduction based on fuzzy-rough techniques", Pattern Recognition Letters, Vol.27, No.5, pp.414-423, 2006.
    Y.H. Qian, et al., "Combination entropy and combination granulation in rough set theory", International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, Vol.16, No.2, pp.179-193, 2008.
    D. Slezak, "Approximate entropy reducts", Fundamenta Informaticae, Vol.53, No.3-4, pp.365-390, 2002.
    G.Y. Wang, et al., "Decision table reduction based on conditional information entropy", Chinese Journal of Computer, Vol.25, No.7, pp.759-766, 2002.
  • 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 (181) PDF downloads(266) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return