Matryoshka Principle for Rule Mining Selection

This paper proposes a method for hiearchical sampling for rule induction. The method generates training samples and test samples in a two-level hierarchical way, and compared the results between these two levels, which corresponding to second- order approximation of estimators in Edgeworth expansion...

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Bibliographic Details
Published in:Procedia computer science 2016, Vol.91, p.1018-1027
Main Authors: Tsumoto, Shusaku, Hirano, Shoji
Format: Article
Language:English
Subjects:
Matryoshka principle
resampling methods
rule mining
rule mining selection
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Online Access:Get full text
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Summary:This paper proposes a method for hiearchical sampling for rule induction. The method generates training samples and test samples in a two-level hierarchical way, and compared the results between these two levels, which corresponding to second- order approximation of estimators in Edgeworth expansion. We applied this method to three medical datasets. The results show that this method gives better performance than conventional methods.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2016.07.139