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

Tohru Asami*, Hachisu Unoki, Kazuo Hashimoto, Seiichi Yamamoto

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


This paper describes the a posteriori method of decision tree learning after the tree is applied to a real domain, such as medical diagnoses. Without collecting a new set of diagnosis examples, the presented algorithm reconstructs a decision tree preserving the error rate of diagnosis from an original tree and a frequency of diagnoses, which is counted at reaching the corresponding terminal node of that tree when applied to a real domain. The new tree has a shorter path length to diagnose and a logically same meaning with the original tree because of generating a set of pseudoexamples whose unobserved attribute values uniformly distribute in the value range. To reduce the computational cost, a method to avoid a generation of a pseudoexample set also is presented. The context dependencies between attributes are considered by introducing an attribute concatenation. The experiments show that an average path length will be reduced by 6 to 10 percent after reconstruction of a randomly generated decision tree with nonoptimized diagnosis frequencies.

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


  • Decision trees
  • ID3
  • inductive learning
  • knowledge acquisition

ASJC Scopus subject areas

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


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