Learning decision lists using homogeneous rules
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Technical Note: Bias and the Quantification of Stability
Machine Learning - Special issue on bias evaluation and selection
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
Machine Learning
Overcoming Process Delays with Decision Tree Induction
IEEE Expert: Intelligent Systems and Their Applications
Machine Learning
Rule Induction with CN2: Some Recent Improvements
EWSL '91 Proceedings of the European Working Session on Machine Learning
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
A Metric for Selection of the Most Promising Rules
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Industry: using decision tree induction to minimize process delays in the printing industry
Handbook of data mining and knowledge discovery
The Journal of Machine Learning Research
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Almost-everywhere algorithmic stability and generalization error
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
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
Advances in Engineering Software
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Most rule learning systems posit hard decision boundaries for continuous attributes and point estimates of rule accuracy, with no measures of variance, which may seem arbitrary to a domain expert. These hard boundaries/points change with small perturbations to the training data due to algorithm instability. Moreover, rule induction typically produces a large number of rules that must be filtered and interpreted by an analyst. This paper describes a method of combining rules over multiple bootstrap replications of rule induction so as to reduce the total number of rules presented to an analyst, to measure and increase the stability of the rule induction process, and to provide a measure of variance to continuous attribute decision boundaries and accuracy point estimates. A measure of similarity between rules is also introduced as a basis of multidimensional scaling to visualize rule similarity. The method was applied to perioperative data and to the UCI (University of California, Irvine) thyroid dataset.