• DocumentCode
    2226986
  • Title

    Attribute reduction of rough sets in mining market value functions

  • Author

    Huang, Jiajin ; Liu, Chunnian ; Ou, Chuangxin ; Yao, Y.Y. ; Zhong, Ning

  • Author_Institution
    Multimedia & Intelligent Software Technol., Beijing Univ. of Technol., China
  • fYear
    2003
  • fDate
    13-17 Oct. 2003
  • Firstpage
    470
  • Lastpage
    473
  • Abstract
    The linear model of market value functions is a new method for direct marketing. Just like other methods in direct marketing, attribute reduction is very important to deal with large databases. We apply the algorithm of attribute reduction, which is based on the combination of rough set theory with the boosting algorithm, to the linear model of market value functions. Experimental results compared with the ELSA/ANN model show that the proposed algorithms can be used effectively in the linear model of market value functions.
  • Keywords
    data mining; marketing; rough set theory; very large databases; ELSA/ANN model; attribute reduction; boosting algorithm; direct marketing; large database; linear model; market value functions; rough set theory; Boosting; Computer science; Data mining; Databases; Entropy; Internet; Laboratories; Predictive models; Rough sets; Set theory;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Web Intelligence, 2003. WI 2003. Proceedings. IEEE/WIC International Conference on
  • Print_ISBN
    0-7695-1932-6
  • Type

    conf

  • DOI
    10.1109/WI.2003.1241242
  • Filename
    1241242
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2226986