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- W180969215 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." @default.
- W180969215 created "2016-06-24" @default.
- W180969215 creator A5073712261 @default.
- W180969215 date "2007-12-02" @default.
- W180969215 modified "2023-09-23" @default.
- W180969215 title "Subset selection algorithm based on mutual information" @default.
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- W180969215 doi "https://doi.org/10.1007/978-3-7908-1709-6_37" @default.
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