• DocumentCode
    2737604
  • Title

    Enterprise Financial Distress Evaluation based on Fuzzy-Rough approach

  • Author

    Lee, Ming-Chang

  • Author_Institution
    Nat. Kaohsiung Univ. of Appl. Sci., Kaohsiung
  • fYear
    2007
  • fDate
    5-7 Sept. 2007
  • Firstpage
    296
  • Lastpage
    296
  • Abstract
    The traditional statistical models assume the covariance matrices for two populations are identical and both populations need to be described by multivariate normal distribution. Clearly, these assumptions do not always reflect the real world. Rough set is a new technique for data mining domain application. In this study, establish a financial distress prediction model using Fuzzy-Rough approach to capture fuzzy rule form data sample. The empirical research which is based on the latest data of Taiwan s listed company, the result shows that this method is high accurate and have reinforcement learning properties and mapping capabilities.
  • Keywords
    covariance matrices; data mining; finance; fuzzy set theory; learning (artificial intelligence); rough set theory; statistical analysis; covariance matrices; data mining domain application; enterprise financial distress evaluation; fuzzy-rough approach; multivariate normal distribution; reinforcement learning; rough set; statistical models; Covariance matrix; Data mining; Fuzzy sets; Fuzzy systems; Gaussian distribution; Knowledge based systems; Learning; Predictive models; Rough sets; Uncertainty;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Innovative Computing, Information and Control, 2007. ICICIC '07. Second International Conference on
  • Conference_Location
    Kumamoto
  • Print_ISBN
    0-7695-2882-1
  • Type

    conf

  • DOI
    10.1109/ICICIC.2007.287
  • Filename
    4427941