Evaluating learning algorithms to support human rule evaluation with predicting interestingness based on objective rule evaluation indices

Hidenao Abe, Shusaku Tsumoto, Miho Ohsaki, Takahira Yamaguchi

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

In this paper, we present an evaluation of learning algorithms of a rule evaluation support method with rule evaluation models based on objective indices for data mining post-processing. Post-processing of mined results is one of the key processes in a data mining process. However, it is difficult for human experts to evaluate several thousands of rules from a large dataset with noises for finding out reraly included valuable rules. 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 twelve rule sets obtained from twelve UCI datasets. With regard to these results, we show the availability of our rule evaluation support method for human experts.

Original languageEnglish
Title of host publicationCommunications and Discoveries from Multidisciplinary Data
Pages269-282
Number of pages14
DOIs
Publication statusPublished - 2008 Sep 15

Publication series

NameStudies in Computational Intelligence
Volume123
ISSN (Print)1860-949X

Keywords

  • Data mining
  • Objective rule evaluation index
  • Post-processing
  • Rule evaluation support

ASJC Scopus subject areas

  • Artificial Intelligence

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    Abe, H., Tsumoto, S., Ohsaki, M., & Yamaguchi, T. (2008). Evaluating learning algorithms to support human rule evaluation with predicting interestingness based on objective rule evaluation indices. In Communications and Discoveries from Multidisciplinary Data (pp. 269-282). (Studies in Computational Intelligence; Vol. 123). https://doi.org/10.1007/978-3-540-78733-4_16