Extraction of domain knowledge from databases based on rough set theory

Sh Tsumoto, H. Tanaka

研究成果: Paper査読

13 被引用数 (Scopus)

抄録

Automated knowledge acquisition is an important research area to solve the bottleneck problem in developing expert systems. For this purpose, there have been proposed several methods of inductive learning, such as induction of decision tress, AQ method, and neural networks. However, most of the approaches focus on inducing rules which classify cases correctly. On the contrary, medical experts also learn other information which is important for medical diagnostic procedures from databases. In this paper, a rule-induction system, called PRIMEROSE3 (Probabilistic Rule Induction Method based on Rough Sets version 3.0), is introduced. This program first analyzes the statistical characteristics of attribute-value pairs from training samples, then determines what kind of diagnosing model can be applied to these training samples. Finally it extracts domain knowledge needed for other diagnostic procedures, based on a selected diagnosing model. PRIMEROSE3 is evaluated on clinical databases on headache, and the induced results are compared with domain knowledge acquired from medical experts. The experimental results show that our proposed method correctly not only selects a diagnosing model, but also extracts domain knowledge.

本文言語English
ページ748-754
ページ数7
出版ステータスPublished - 1996 12月 1
イベントProceedings of the 1996 5th IEEE International Conference on Fuzzy Systems. Part 3 (of 3) - New Orleans, LA, USA
継続期間: 1996 9月 81996 9月 11

Other

OtherProceedings of the 1996 5th IEEE International Conference on Fuzzy Systems. Part 3 (of 3)
CityNew Orleans, LA, USA
Period96/9/896/9/11

ASJC Scopus subject areas

  • ソフトウェア
  • 理論的コンピュータサイエンス
  • 人工知能
  • 応用数学

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