Subset selection algorithm based on mutual information

Moon Y. Huh ()
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Moon Y. Huh: Sungkyunkwan University

A chapter in Compstat 2006 - Proceedings in Computational Statistics, 2006, pp 461-470 from Springer

Abstract: 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.

Keywords: Variable selection; mutual information; normal mixture; EM algorithm (search for similar items in EconPapers)
Date: 2006
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-7908-1709-6_37

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DOI: 10.1007/978-3-7908-1709-6_37

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