PRIMEROSE: PROBABILISTIC RULE INDUCTION METHOD BASED ON ROUGH SETS AND RESAMPLING METHODS

S. Tsumoto, H. Tanaka

Research output: Contribution to journalArticlepeer-review

67 Citations (Scopus)

Abstract

Automated knowledge acquisition is an important research issue in machine learning. Several methods of inductive learning, such as ID3 family and AQ family, have been applied to discover meaningful knowledge from large databases and their usefulness is assured in several aspects. However, since their methods are of a deterministic nature and the reliability of acquired knowledge is not evaluated statistically, these methods are ineffective when applied to domains essentially probabilistic in nature, such as medical domains. Extending concepts of rough set theory to a probabilistic domain, we introduce a new approach to knowledge acquisition, which induces probabilistic rules based on rough set theory (PRIMEROSE) and develop a program that extracts rules for an expert system from a clinical database, using this method. The results show that the derived rules almost correspond to those of the medical experts.

Original languageEnglish
Pages (from-to)389-405
Number of pages17
JournalComputational Intelligence
Volume11
Issue number2
DOIs
Publication statusPublished - 1995 May

Keywords

  • cross‐validation
  • knowledge acquisition
  • machine learning
  • rough sets

ASJC Scopus subject areas

  • Computational Mathematics
  • Artificial Intelligence

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