Learning complicated concepts reliably and usefully
COLT '88 Proceedings of the first annual workshop on Computational learning theory
Fast discovery of association rules
Advances in knowledge discovery and data mining
A Minimization Approach to Propositional Inductive Learning
ECML '95 Proceedings of the 8th European Conference on Machine Learning
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Inconsistency Tests for Patient Records in a Coronary Heart Disease Database
ISMDA '00 Proceedings of the First International Symposium on Medical Data Analysis
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This paper presents a novel approach to the construction of reliable diagnostic rules from the available cases with known diagnoses. It proposes a simple and general framework based on the generation of the so-called confirmation rules. A property of a system of confirmation rules is that it allows for indecisive answers, which, as a consequence, enables that all decisive answers proposed by the system are reliable. Moreover, the consensus of two or more confirmation rules additionally increases the reliability of diagnostic answers. Experimental results in the problem of coronary artery disease diagnosis illustrate the approach.