• DocumentCode
    598649
  • Title

    Experiments on rule induction from incomplete data using three probabilistic approximations

  • Author

    Clark, Patrick G. ; Grzymala-Busse, Jerzy W.

  • Author_Institution
    Department of Electrical Eng. and Computer Sci., University of Kansas, Lawrence, 66045, USA
  • fYear
    2012
  • fDate
    11-13 Aug. 2012
  • Firstpage
    78
  • Lastpage
    83
  • Abstract
    We present results of experiments on rule induction using three probabilistic approximations: lower, middle, and upper. Our results were conducted on four typical series of incomplete data sets with 5% increments of missing attribute values. Two interpretations of missing attribute values were used: lost and “do not care” conditions. We conclude that the best approach (choice of the interpretation of missing attribute values and selection of the best type of approximation) depends on a data set. Probabilistic approximations are constructed from characteristic sets. The number of distinct probabilities associated with characteristic sets is much larger for data sets with “do not care” conditions than with data sets with lost values. Therefore, for data sets with “do not care” conditions the number of probabilistic approximations is also larger.
  • Keywords
    Approximation methods; Data mining; MLEM2 rule induction algorithm; parameterized approximations; probabilistic approximations; rough set theory;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Granular Computing (GrC), 2012 IEEE International Conference on
  • Conference_Location
    Hangzhou, China
  • Print_ISBN
    978-1-4673-2310-9
  • Type

    conf

  • DOI
    10.1109/GrC.2012.6468610
  • Filename
    6468610