Evaluating learning algorithms with meta-learning schemes for a rule evaluation support method based on objective indices

Authors:
Hidenao Abe;Shusaku Tsumoto;Miho Ohsaki;Hideto Yokoi;Takahira Yamaguchi
Affiliations:
Department of Medical Informatics, Shimane University, School of Medicine, Shimane, Japan;Department of Medical Informatics, Shimane University, School of Medicine, Shimane, Japan;Faculty of Engineering, Doshisha University;Department of Medical Informatics, Kagawa University Hospital;Faculty of Science and Technology, Keio University
Venue:
PKAW'06 Proceedings of the 9th Pacific Rim Knowledge Acquisition international conference on Advances in Knowledge Acquisition and Management
Year:
2006

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Abstract

In this paper, we present evaluations of learning algorithms for a novel rule evaluation support method in data mining post-processing, which is one of the key processes in a data mining process. It is difficult for human experts to evaluate many thousands of rules from a large dataset with noises completely. To reduce the costs of rule evaluation task, we have developed the rule evaluation support method with rule evaluation models, which are learned from a dataset consisted of objective indices and evaluations of a human expert for each rule. To enhance adaptability of rule evaluation models, we introduced a constructive meta-learning system to choose proper learning algorithms for constructing them. Then, we have done a case study on the meningitis data mining result, the hepatitis data mining results and rule sets from the eight UCI datasets.