Decision tree learning system with switching evaluator

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトルAdvances in Artificial Intelligence - 11th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, AI 1996, Proceedings
出版社Springer Verlag
ページ349-361
ページ数13
1081
ISBN(印刷版)3540612912, 9783540612919
DOI
出版ステータスPublished - 1996
外部発表はい
イベント11th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, AI 1996 - Toronto, Canada
継続期間: 1996 5 211996 5 24

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
1081
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Other

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

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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