• DocumentCode
    424234
  • Title

    Variable precision rough set model based on general relation

  • Author

    Gong, Zeng-tai ; Sun, Bing-zhen ; Shao, Ya-Bin ; Chen, De-gang ; He, Qiang

  • Author_Institution
    Coll. of Math. & Inf. Sci., Northwest Normal Univ., Lanzhou, China
  • Volume
    4
  • fYear
    2004
  • fDate
    26-29 Aug. 2004
  • Firstpage
    2490
  • Abstract
    To make up for the drawbacks of the rough set model on general relation, a majority inclusion relation is defined and an error parameter α is introduced in order to give a variable precision rough set model based on general relation. In our rough set model, if α satisfies some conditions, then the model degenerates the basic rough set model which was first introduced by Z. Pawlak or degenerate the graded rough set model. What follows that model proposed in this paper is an extension of the classical basic rough set based on general relation and graded rough set model. After introducing the error parameter α, more useful information and data are collected and mined. Thus, the drawbacks, which lose more useful information for demanding the inclusion of the absolutely precision in the classical basic rough set model, are overcome.
  • Keywords
    rough set theory; error parameter; majority inclusion relation; variable precision rough set model; Data mining; Databases; Educational institutions; Electronic mail; Information science; Mathematical model; Mathematics; Set theory; Sun; Uncertainty;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on
  • Print_ISBN
    0-7803-8403-2
  • Type

    conf

  • DOI
    10.1109/ICMLC.2004.1382222
  • Filename
    1382222