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

摘要

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.