Learning decision lists using homogeneous rules
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Machine Learning
Machine learning as massive search
Machine learning as massive search
Machine Learning for the Detection of Oil Spills in Satellite Radar Images
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Bootstrapping rule induction to achieve rule stability and reduction
Journal of Intelligent Information Systems
Backward chaining rule induction
Intelligent Data Analysis - Selected papers from IDA2005, Madrid, Spain
Searching for meaningful feature interactions with backward-chaining rule induction
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
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Most rule learning systems posit hard decision boundariesfor continuous attributes and point estimates of ruleaccuracy, with no measures of variance, which may seemarbitrary to a domain expert. These hard boundaries/pointschange with small perturbations to the training data. Moreover,rule induction typically produces a large number ofrules that must be filtered and interpreted by an analyst.This paper describes a method of combining rules over multiplebootstrap replications of rule induction so as to reducethe total number of rules presented to an analyst and to providemeasures of variance to continuous attribute decisionboundaries and accuracy-point estimates. The method isillustrated with perioperative data.