Evaluating learning models with transitions of human interests based on objective rule evaluation indices

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

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

Abstract

This paper presents a method to support the evaluation procedure of a data mining process using human-system interaction. The post-processing of mined results is one of the key factors for successful data mining process. However, it is difficult for human experts to completely evaluate several thousands of rules from a large dataset containing noise. We have designed a method based on objective rule evaluation indices to support the rule evaluation procedure; the indices are calculated to evaluate each if-then rule mathematically. We have evaluated five representative learning algorithms to construct rule evaluation models of the actual data mining results from a chronic hepatitis data set. Further, we discuss the relationship between the transitions of the subjective criterion of a medical expert and the performances of the rule evaluation models.

Original languageEnglish
Title of host publicationMEDINFO 2007 - Proceedings of the 12th World Congress on Health (Medical) Informatics
Subtitle of host publicationBuilding Sustainable Health Systems
PublisherIOS Press
Pages581-585
Number of pages5
ISBN (Print)9781586037741
Publication statusPublished - 2007
Event12th World Congress on Medical Informatics, MEDINFO 2007 - Brisbane, QLD, Australia
Duration: 2007 Aug 202007 Aug 24

Publication series

NameStudies in Health Technology and Informatics
Volume129
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

Other

Other12th World Congress on Medical Informatics, MEDINFO 2007
CountryAustralia
CityBrisbane, QLD
Period07/8/2007/8/24

Keywords

  • data mining
  • human interest
  • post-processing
  • rule evaluation index

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

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