Reconstruction of a decision tree with learning examples generated from an original tree and its characteristics

Tohru Asami, Hachisu Unoki, Kazuo Hashimoto, Seiichi Yamamoto

Research output: Contribution to journalArticle

Abstract

An a posteriori approach to decision tree learning is proposed. The method, which reconstructs a decision tree during application to a real domain, preserves the previous error rate of diagnosis and frequency of diagnosis for the current tree. It also contains a scheme for eliminating the generation of pseudoexample sets to reduce computation time.

Original languageEnglish
Pages (from-to)93-105
Number of pages13
JournalSystems and Computers in Japan
Volume25
Issue number9
Publication statusPublished - 1994 Aug
Externally publishedYes

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ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Hardware and Architecture
  • Information Systems
  • Theoretical Computer Science

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