NDLI: Evaluating Learning Algorithms to Construct Rule Evaluation Models Based on Objective Rule Evaluation Indices

Content Provider	IEEE Xplore Digital Library
Author	Abe, H. Tsumoto, S. Ohsaki, M. Yamaguchi, T.
Copyright Year	2007
Description	Author affiliation: Shimane Univ., Matsue (Abe, H.; Tsumoto, S.)
Abstract	In the present paper, we describe an evaluation of our rule evaluation support method with constructive meta- learning scheme for post-processing of mined results with rule evaluation models based on objective indices. Postprocessing of mined results is one of the key processes in the data mining process. However, it is difficult for human experts to completely evaluate several thousand of rules from a large dataset with noises. To reduce the costs in such a rule evaluation task, we have developed a rule evaluation support method with rule evaluation models, which learn from objective indices for mined classification rules and evaluations by a human expert for each rule. To enhance the adaptability of rule evaluation models, we introduced a constructive meta-learning scheme to choose proper learning algorithms. Then, we performed the case study on the meningitis data mining as an actual problem. In addition, we evaluated the proposed method using the ten rule sets obtained from the ten UCI datasets. The obtained results demonstrate the applicability of the proposed rule evaluation support method.
Starting Page	212
Ending Page	221
File Size	3364671
Page Count	10
File Format	PDF
ISBN	9781424413270
DOI	10.1109/COGINF.2007.4341893
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-06
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Humans Data mining Cognitive informatics Paper technology Costs Statistical analysis Learning systems Database systems Machine learning algorithms Noise generators Rule evaluation support Data Mining Post-processing Rule evaluation index
Content Type	Text
Resource Type	Article

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