Advertisement

Rule Evaluation Model as Behavioral Modeling of Domain Experts

Conference paper

In this paper, we present an experiment to describe behavior of a human decision on the rule evaluation procedure, which is a post-processing procedure in data mining process, based on objective rule indices. The post-processing of mined results is one of the key factors for successful data mining process. However, the relationship between transitions of human criteria and the objective rule evaluation indices has never been clarified as behavioral viewpoints. By using a method based on objective rule evaluation indices to support the rule evaluation procedure, we have evaluated the accuracies of 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 criteria of a medical expert and the rule evaluation models.

Keywords

Data Mining Human Expert Medical Expert Human Evaluation Rule Evaluation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Hilderman R.J. and Hamilton H.J. (2001) Knowledge discovery and measures of interest. Kluwer Academic PublishersGoogle Scholar
  2. 2.
    Tan P.N., Kumar V., and Srivastava J. (2003) Selecting the right interestingness measure for association patterns. In: Proc. of International Conference on Knowledge Discovery and Data Mining, 32–41Google Scholar
  3. 3.
    Yao Y.Y. and Zhong N. (1999) An analysis of quantitative measures associated with rules. In: Proc. of Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD-1999, 479–488Google Scholar
  4. 4.
    Ohsaki M., Sato Y., Kume S., Yokoi H., and Yamaguchi T. (2004) Comparison between objective interestingness measures and real human interests in medical data mining. In: Proc. of the 17th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems IEA/AIE-2004, LNAI 3029, 1072–1081Google Scholar
  5. 5.
    Abe H., Ohsaki M., Yokoi H., and Yamaguchi T. (2006) Implementing an integrated time-series data mining environment based on temporal pattern extraction methods? A case study of an interferon therapy risk mining for chronic hepatitis, In: JSAI2005 Workshops, LNAI 4012, 425–435Google Scholar
  6. 6.
    Ohsaki M., Kitaguchi S., Kume S., Yokoi H., and Yamaguchi T. Evaluation of rule interestingness measures with a clinical dataset on hepatitis. In: Proc. of ECML/PKDD-2004, LNAI 3202, 362–373Google Scholar
  7. 7.
    Ohsaki M., Abe H., Yokoi H., Tsumoto S., and Yamaguchi T. (2007) Evaluation of Interestingness Measures in Medical Knowledge Discovery in Databases, Artificial Intelligence in Medicine, 41(3), 177–196CrossRefGoogle Scholar
  8. 8.
    Witten I.H. and Frank E. (2000) Data Mining: Practical machine learning tools and techniques with Java implementations, Morgan KaufmannGoogle Scholar
  9. 9.
    Quinlan J.R. (1993) Programs for machine learning, Morgan KaufmannGoogle Scholar
  10. 10.
    Runmelhart D. E., McClelland J. L. (1986) Parallel Distribute Processing, MIT PressGoogle Scholar
  11. 11.
    Platt J. (1999) Fast training of support vector machines using sequential minimal optimization. In: Schoelkopf B., Burges C., and Smola A. (eds.), Advances in Kernel Methods-Support Vector Learning, MIT Press, 185–208Google Scholar
  12. 12.
    Frank, E., Wang, Y., Inglis, S., Holmes, G., and Witten, I.H. (1998) Using model trees for classification. Machine Learning 32(1) 63–76MATHCrossRefGoogle Scholar
  13. 13.
    Holte R.C. (1993) Very simple classification rules perform well on most commonly used datasets. Machine Learning, 11, 63?–91CrossRefGoogle Scholar

Copyright information

© Springer-Verlag US 2009

Authors and Affiliations

  1. 1.Shimane UniversityJapan

Personalised recommendations