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Subset selection algorithm based on mutual information

In: Compstat 2006 - Proceedings in Computational Statistics

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  • Moon Y. Huh

    (Sungkyunkwan University)

Abstract

Best subset selection problem is one of the classical problems in statistics and in data mining. When variables of concern are continuous types, the problem is classical in classical regression problems. Most of the data mining techniques including decision trees are designed to handle discrete type variables only. With complex data, most of the data mining techniques first transform continuous variables into discrete variables before applying the techniques. Hence the result depends on the discretiztion method applied. This paper proposes an algorithm to select a best subset using the original data set. The algorithm is based on mutual information (MI) introduced by Shannon [Shan48]. It computes MI’s of up to two-dimensional variables: both continuous, both discrete, or one continuous and one discrete. It has and automatic stopping criterion when appropriate subset is selected.

Suggested Citation

  • Moon Y. Huh, 2006. "Subset selection algorithm based on mutual information," Springer Books, in: Alfredo Rizzi & Maurizio Vichi (ed.), Compstat 2006 - Proceedings in Computational Statistics, pages 461-470, Springer.
    • Handle: RePEc:spr:sprchp:978-3-7908-1709-6_37
    DOI: 10.1007/978-3-7908-1709-6_37
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