Learning complicated concepts reliably and usefully
COLT '88 Proceedings of the first annual workshop on Computational learning theory
Machine Learning
Error reduction through learning multiple descriptions
Machine Learning
Fast discovery of association rules
Advances in knowledge discovery and data mining
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Relevancy Filter for Constructive Induction
IEEE Intelligent Systems
Experiments with Noise Filtering in a Medical Domain
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Generating Actionable Knowledge by Expert-Guided Subgroup Discovery
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Coronary Heart Disease Patient Models Based on Inductive Machine Learning
AIME '01 Proceedings of the 8th Conference on AI in Medicine in Europe: Artificial Intelligence Medicine
From local to global patterns: evaluation issues in rule learning algorithms
LPD'04 Proceedings of the 2004 international conference on Local Pattern Detection
Active subgroup mining: a case study in coronary heart disease risk group detection
Artificial Intelligence in Medicine
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The concept of confirmation rule sets represents a framework for reliable decision making that combines two principles that are effective for increasing the predictive accuracy: consensus in an ensemble of classifiers and indecisive or probabilistic predictions in cases when reliable decisions are not possible. The confirmation rules concept uses a separate classifier set for every class of the domain. In this decision model different rules can be incorporated: either those obtained by applying one or more inductive learning algorithms or even rules representing human encoded expert domain knowledge. The only conditions for the inclusion of a rule into the confirmation rule set are its high predictive value and relative independence of other rules in the confirmation rule set. This paper introduces the concept of confirmation rule sets, together with an algorithm for selecting relatively independent rules from a set of all acceptable confirmation rules and an algorithm for the systematic construction of a set of confirmation rules.