Induction of probabilistic rules based on rough set theory

Shusaku Tsumoto, Hiroshi Tanaka

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

5 Citations (Scopus)

Abstract

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

Original languageEnglish
Title of host publicationAlgorithmic Learning Theory - 4th International Workshop, ALT 1993, Proceedings
EditorsKlaus P. Jantke, Shigenobu Kobayashi, Etsuji Tomita, Takashi Yokomori
PublisherSpringer Verlag
Pages410-423
Number of pages14
ISBN (Print)9783540573708
DOIs
Publication statusPublished - 1993
Event4th Workshop on Algorithmic Learning Theory, ALT 1993 - Tokyo, Japan
Duration: 1993 Nov 81993 Nov 10

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume744 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other4th Workshop on Algorithmic Learning Theory, ALT 1993
CountryJapan
CityTokyo
Period93/11/893/11/10

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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