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

Hidenao Abe, Shusaku Tsumoto, Miho Ohsaki, Hideto Yokoi, Takahira Yamaguchi

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish
Title of host publicationAdvances in Knowledge Acquisition and Management - Pacific Rim Knowledge Acquisition Workshop, PKAW 2006, Revised Selected Papers
EditorsAchim Hoffmann, Byeong-ho Kang, Debbie Richards, Shusaku Tsumoto
PublisherSpringer Verlag
Number of pages14
ISBN (Print)9783540689553
Publication statusPublished - 2006
EventPacific Rim Knowledge AcquisitionWorkshop, PKAW 2006 - Guilin, China
Duration: 2006 Aug 72006 Aug 8

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4303 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


OtherPacific Rim Knowledge AcquisitionWorkshop, PKAW 2006

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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