On the handling of continuous-valued attributes in decision tree generation View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

1992-01

AUTHORS

Usama M. Fayyad, Keki B. Irani

ABSTRACT

We present a result applicable to classification learning algorithms that generate decision trees or rules using the information entropy minimization heuristic for discretizing continuous-valued attributes. The result serves to give a better understanding of the entropy measure, to point out that the behavior of the information entropy heuristic possesses desirable properties that justify its usage in a formal sense, and to improve the efficiency of evaluating continuous-valued attributes for cut value selection. Along with the formal proof, we present empirical results that demonstrate the theoretically expected reduction in evaluation effort for training data sets from real-world domains. More... »

PAGES

87-102

References to SciGraph publications

  • 1989-03. The CN2 induction algorithm in MACHINE LEARNING
  • 1986-03. Induction of decision trees in MACHINE LEARNING
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/bf00994007

    DOI

    http://dx.doi.org/10.1007/bf00994007

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1018294482


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