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
Information Sciences: an International Journal
Incremental Induction of Decision Trees
Machine Learning
Machine Learning
Modelling Medical Diagnostic Rules Based on Rough Sets
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
Incremental rule induction based on rough set theory
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
Incremental rules induction based on rule layers
RSKT'12 Proceedings of the 7th international conference on Rough Sets and Knowledge Technology
Analysis of symmetry properties for bayesian confirmation measures
RSKT'12 Proceedings of the 7th international conference on Rough Sets and Knowledge Technology
Rough Sets and Knowledge Technology: 7th International Conference, RSKT 2012, Chengdu, China, August 17-20, 2012
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This paper proposes a new framework for incremental rule induction of medical diagnostic rules based on incremental sampling scheme and rule layers. When an example is appended, four possibilities can be considered. Thus, updates of accuracy and coverage are classified into four cases, which give two important inequalities of accuracy and coverage for induction of probabilistic rules. By using these two inequalities, the proposed method classifies a set of formulae into four layers: the rule layer, subrule layer in and out and the non-rule layer. Then, the obtained rule and subrule layers play a central role in updating proabilistic rules. If a new example contributes to an increase in the accuracy and coverage of a formula in the subrule layer, the formula is moved into the rule layer. If this contributes to a decrease of a formula in the rule layer, the formula is moved into the subrule layer. The proposed method was evaluated on a dataset regarding headaches, whose results show that the proposed method outperforms the conventional methods.