Consultation system for diagnosis of headache and facial pain: `RHINOS”
Proceedings of the 4th conference on Logic programming '85
C4.5: programs for machine learning
C4.5: programs for machine learning
Variable precision rough set model
Journal of Computer and System Sciences
From rough set theory to evidence theory
Advances in the Dempster-Shafer theory of evidence
Information Sciences: an International Journal
Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
Readings in Machine Learning
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Outlier detection method based on hybrid rough: negative using PSO algorithm
Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication
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One of the most important problems on rule induction methods is that extracted rules partially represent information on experts' decision processes, which makes rule interpretation by domain experts difficult. In order to solve this problem, the characteristics of medical reasoning is discussed positive and negative rules are introduced which model medical experts' rules. Then, for induction of positive and negative rules, two search algorithms are provided. The proposed rule induction method was evaluated on medical databases, the experimental results of which show that induced rules correctly represented experts' knowledge and several interesting patterns were discovered.