• DocumentCode
    1934919
  • Title

    Rule Induction Based on Fuzzy Rough Sets

  • Author

    Tsang, Eric C C ; Zhao, Su-yun ; Lee, John W T

  • Author_Institution
    Hong Kong Polytech. Univ., Kowloon
  • Volume
    5
  • fYear
    2007
  • fDate
    19-22 Aug. 2007
  • Firstpage
    3028
  • Lastpage
    3033
  • Abstract
    In this paper, we propose one method of rule induction based on fuzzy rough set. First, the consistence degree is proposed as the basic concept to induce rules based on fuzzy rough sets. The concepts of rule induction, such as value reduct, reduct rule and so on, are then proposed based on the definition of consistence degree. Second, a discernibility array is constructed, and then an algorithm to find the reduct rule using the discernibility array is designed. Finally, the numerical experimental results demonstrate that the method of rule induction proposed in this paper is feasible. The key idea of this paper is that the value reduct (i.e. reduct rule) keeps the consistence degree invariant. The main contribution of this paper is introduction of rule induction based on fuzzy rough sets using the concept of fuzzy lower and upper approximation.
  • Keywords
    fuzzy set theory; inference mechanisms; knowledge based systems; rough set theory; consistence degree; discernibility array; fuzzy approximation; fuzzy rough set; reduct rule; rule induction; value reduct; Algorithm design and analysis; Approximation algorithms; Computer science; Cybernetics; Fuzzy sets; Machine learning; Mathematical model; Mathematics; Rough sets; Set theory; Discernibility array; Fuzzy rough set; Rule induction; Triangular norm;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics, 2007 International Conference on
  • Conference_Location
    Hong Kong
  • Print_ISBN
    978-1-4244-0973-0
  • Electronic_ISBN
    978-1-4244-0973-0
  • Type

    conf

  • DOI
    10.1109/ICMLC.2007.4370667
  • Filename
    4370667