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

Abstract : One of the most important problems in rule induction methods is how to estimate which method is the best to use in an applied domain. While some methods are useful in some domains, they are not useful in other domains. Therefore it is very difficult to choose one of these methods. For this purpose, we introduce multiple testing based on recursive iteration of resampling methods for rule-induction (MULT-RECITE-R). We applied this MULT-RECITE-R method to monk datasets in UCI data repository. The results show that this method gives the best selection of estimation methods in almost the all cases.
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Zhongzhi Shi; Sunil Vadera; Agnar Aamodt; David Leake. 6th IFIP TC 12 International Conference on Intelligent Information Processing (IIP), Oct 2010, Manchester, United Kingdom. Springer, IFIP Advances in Information and Communication Technology, AICT-340, pp.139-144, 2010, Intelligent Information Processing V. 〈10.1007/978-3-642-16327-2_19〉
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Shusaku Tsumoto, Shoji Hirano, Hidenao Abe. Automated Empirical Selection of Rule Induction Methods Based on Recursive Iteration of Resampling Methods. Zhongzhi Shi; Sunil Vadera; Agnar Aamodt; David Leake. 6th IFIP TC 12 International Conference on Intelligent Information Processing (IIP), Oct 2010, Manchester, United Kingdom. Springer, IFIP Advances in Information and Communication Technology, AICT-340, pp.139-144, 2010, Intelligent Information Processing V. 〈10.1007/978-3-642-16327-2_19〉. 〈hal-01060360〉

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