Improved Use of Continuous Attributes in C4.5

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Improved Use of Continuous Attributes in C4.5 (1996)

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by J. R. Quinlan

Venue:	Journal of Artificial Intelligence Research
Citations:	281 - 1 self

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@ARTICLE{Quinlan96improveduse,
    author = {J. R. Quinlan},
    title = {Improved Use of Continuous Attributes in C4.5},
    journal = {Journal of Artificial Intelligence Research},
    year = {1996},
    volume = {4},
    pages = {77--90}
}

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Abstract

A reported weakness of C4.5 in domains with continuous attributes is addressed by modifying the formation and evaluation of tests on continuous attributes. An MDL-inspired penalty is applied to such tests, eliminating some of them from consideration and altering the relative desirability of all tests. Empirical trials show that the modifications lead to smaller decision trees with higher predictive accuracies. Results also confirm that a new version of C4.5 incorporating these changes is superior to recent approaches that use global discretization and that construct small trees with multi-interval splits. 1. Introduction Most empirical learning systems are given a set of pre-classified cases, each described by a vector of attribute values, and construct from them a mapping from attribute values to classes. The attributes used to describe cases can be grouped into continuous attributes, whose values are numeric, and discrete attributes with unordered nominal values. For example, the de...

Keyphrases

continuous attribute attribute value empirical learning system predictive accuracy decision tree relative desirability new version discrete attribute unordered nominal value pre-classified case small tree recent approach empirical trial mdl-inspired penalty multi-interval split global discretization reported weakness

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