Constructing Binary Classification Trees with High Intelligibility
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    Abstract:

    Binarization is the most popular discretization method in decision tree generation, while for the domain with many continuous attributes, it always gets a big incomprehensible tree which can't be described as knowledge. In order to get a more intelligible decision tree, this paper presents a new discretization algorithm, RCAT, for continuous attributes in the generation of binary classification tree. It uses simple binarization to solve the multisplitting problem through mapping a continuous attribute into another probability attribute based on statistic information. Two pruning methods are introduced to simplify the constructed tree. Empirical results of several domains show that, for the two-class problem with a preponderance of continuous attributes, RCAT algorithm can generate a much smaller decision tree efficiently with higher intelligibility than binarization while retaining predictive accuracy.

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蒋艳凰,杨学军,赵强利.具有高可理解性的二分决策树生成算法研究.软件学报,2003,14(12):1996-2005

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  • Received:October 29,2002
  • Revised:December 31,2002
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