Decision tree learning system with switching evaluator

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we introduce the notion of the local strategy of constructing decision trees that includes the information theoretic entropy algorithm in ID3 (or C4.5) and any other local algorithms. Simply put, given a sample, a local algorithm constructs a decision tree in the top-down manner using an evaluation function. We propose a new local algorithm that is very different from the entropy algorithm. We analyze behaviors of the two algorithms on a simple model. Based on these analyses, we propose a learning system of decision trees which can change an evaluation function while constructing decision trees, and verify the effect of the system by experiments with real databases. The system not only achieves a high accuracy, but also produces well-balanced decision trees, which have the advantage of fast classification.

Original languageEnglish
Title of host publicationAdvances in Artificial Intelligence - 11th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, AI 1996, Proceedings
PublisherSpringer Verlag
Pages349-361
Number of pages13
Volume1081
ISBN (Print)3540612912, 9783540612919
DOIs
Publication statusPublished - 1996
Externally publishedYes
Event11th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, AI 1996 - Toronto, Canada
Duration: 1996 May 211996 May 24

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1081
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other11th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, AI 1996
CountryCanada
CityToronto
Period96/5/2196/5/24

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Koshiba, T. (1996). Decision tree learning system with switching evaluator. In Advances in Artificial Intelligence - 11th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, AI 1996, Proceedings (Vol. 1081, pp. 349-361). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1081). Springer Verlag. https://doi.org/10.1007/3-540-61291-2_64