Evaluating learning algorithms for a rule evaluation support method based on objective rule evaluation indices

Authors:
Hidenao Abe;Shusaku Tsumoto;Miho Ohsaki;Takahira Yamaguchi
Affiliations:
Department of Medical Informatics, Shimane University, School of Medicine, Izumo, Shimane, Japan;Department of Medical Informatics, Shimane University, School of Medicine, Izumo, Shimane, Japan;Faculty of Engineering, Doshisha University, Kyo-Tanabe, Kyoto, Japan;Faculty of Science and Technology, Keio University, Kohoku Yokohama, Kanagawa, Japan
Venue:
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
Year:
2006

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Abstract

In this paper, we present an evaluation of learning algorithms of a novel rule evaluation support method for post-processing of mined results with rule evaluation models based on objective indices. Post-processing of mined results is one of the key processes in a data mining process. However, it is difficult for human experts to completely evaluate several thousands of rules from a large dataset with noises. To reduce the costs in such rule evaluation task, we have developed the rule evaluation support method with rule evaluation models which learn from a dataset. This dataset comprises objective indices for mined classification rules and evaluations by a human expert for each rule. To evaluate performances of learning algorithms for constructing the rule evaluation models, we have done a case study on the meningitis data mining as an actual problem. Furthermore, we have also evaluated our method with five rule sets obtained from five UCI datasets.