Induction of probabilistic rules based on rough set theory

Shusaku Tsumoto, Hiroshi Tanaka

研究成果: Conference contribution

5 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルAlgorithmic Learning Theory - 4th International Workshop, ALT 1993, Proceedings
編集者Klaus P. Jantke, Shigenobu Kobayashi, Etsuji Tomita, Takashi Yokomori
出版社Springer Verlag
ページ410-423
ページ数14
ISBN(印刷版)9783540573708
DOI
出版ステータスPublished - 1993
イベント4th Workshop on Algorithmic Learning Theory, ALT 1993 - Tokyo, Japan
継続期間: 1993 11 81993 11 10

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)
744 LNAI
ISSN(印刷版)0302-9743
ISSN(電子版)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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