Rule Discovery by Probabilistic Rough Induction

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概要

• 論文の詳細を見る

• In this paper, we propose a probabilistic rough induction approach called GDT-RS that is based on the combination of Generalization Distribution Table (GDT) and the Rough Set methodology. A GDT is a table in which the probabilistic relationships between concepts and instances over discrete domains are represented. The GDT provides a probabilistic basis for evaluating the strength of a rule. Furthermore, the rough set methodology is used to find minimal relative reducts from the set of rules with larger strengths. Main features of our approach are (1) biases can be flexibly selected for search control, and background knowledge can be used as a bias to control the creation of a GDT and the rule discovery process; (2)unseen instances are considered in rule discovery process and the uncertainty of a rule, including its ability to predict possible instances, can be explicitly represented in the strength of the rule.

• 社団法人人工知能学会の論文
• 2000-03-01

著者

• Zhong Ning

  Faculty Of Engineering Yamaguchi University.

• Dong Juzhen

  Graduate School of Science and Engineering, Yamaguchi University.

• Ohsuga Setsuo

  School of Science and Engineering, Waseda University.

• Dong Juzhen

  Graduate School Of Science And Engineering Yamaguchi University.

• Ohsuga Setsuo

  School Of Science And Engineering Waseda University.

関連論文

• Rule Discovery by Probabilistic Rough Induction

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