Automated Empirical Selection of Rule Induction Methods Based on Recursive Iteration of Resampling Methods and Multiple Testing

Authors:
Shusaku Tsumoto;Shoji Hirano
Affiliations:
-;-
Venue:
ICDMW '10 Proceedings of the 2010 IEEE International Conference on Data Mining Workshops
Year:
2010

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Information flow metrics analysis in object oriented programming and metrics validation process by RAA algorithm

Advances in Engineering Software

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Abstract

This paper proposes a method for multiple testing based on recursive iteration of resampling methods 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 Edge worth expansion. We applied this MULT-RECITE-R method to three newly collected medical databases and seven UCI databases. The results show that this method gives the best selection of estimation methods in almost the all cases.