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Rule Quality Measures Improve the Accuracy of Rule Induction: An Experimental Approach

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Foundations of Intelligent Systems (ISMIS 2000)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1932))

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Abstract

Rule quality measures can help to determine when to stop ge- neralization or specification of rules in a rule induction system. Rule quality measures can also help to resolve conflicts among rules in a rule classification system. We enlarge our previous set of statistical and empirical rule quality formulas which we tested earlier on a number of standard machine learning data sets. We describe this new set of formulas, performing extensive tests which also go beyond our earlier tests, to compare these formulas. We also specify how to generate formula-behavior ru- les from our experimental results, which show the relationships between a formula’s performance and the characteristics of a dataset. Formula- behavior rules can be combined into formula-selection rules which can select a rule quality formula before rule induction takes place. We report the experimental results showing the effects of formula-selection on the predictive performance of a rule induction system.

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References

  1. Ali, K. and Pazzani, M. 1993. “HYDRA: A noise-tolerant relational concept learning algorithm”. Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence (IJCAI’93), Chambery, France. Morgan Kaufmann.

    Google Scholar 

  2. An, A. and Cercone, N. 1998. “ELEM2: A Learning System for More Accurate Classifications.” Lecture Notes in Artificial Intelligence 1418.

    Google Scholar 

  3. An, A. and Cercone, N. 1999. “An Empirical Study on Rule Quality Measures”, Proceedings of the Seventh International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing, Yamaguchi, Japan.

    Google Scholar 

  4. Arkin, H. and Colton, R. R. 1970. Statistical Methods. Barnes & Noble Inc., New York.

    Google Scholar 

  5. Bishop, Y.M.M, Fienberg, S.E. and Holand, P.W. 1991. Discrete Multivariate Analysis: Theory and Practice. The MIT Press.

    Google Scholar 

  6. Brazdil, P. and Torgo, L. 1990. “Knowledge Acquisition via Knowledge Integration”. In: Current Trends in Knowledge Acquisition, IOS Press.

    Google Scholar 

  7. Bruha, I. 1993. “Quality of Decision Rules: Empirical and Statistical Approaches”. Informatica, 17, pp.233–243.

    Google Scholar 

  8. Bruha, I. 1996. “Quality of Decision Rules: Definitions and Classification Schemes for Multiple Rules”, in Nakhaeizadeh, G. and Taylor, C. C. (eds.): Machine Learning and Statistics, The Interface. Jone Wiley & Sons Inc.

    Google Scholar 

  9. Cohen, J. 1960. “A Coefficient of Agreement for Nominal Scales”. Educational and Psych. Meas. 22, pp.37–46.

    Google Scholar 

  10. Duda, R., Gaschnig, J. and Hart, P. 1979. “Model Design in the Prospector Consultant System for Mineral Exploration”. In D. Michie (ed.), Expert Systems in the Micro-electronic Age. Edinburgh University Press, Edinburgh, UK.

    Google Scholar 

  11. Holte, R., Acker, L. and Porter, B. 1989. “Concept Learning and the Problem of Small Disjuncts”. Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, Detroit, Michigan.

    Google Scholar 

  12. Kononenko, I. and Bratko, I. 1991. “Information-Based Evaluation Criterion for Classifier’s Performance”. Machine Learning, 6, 67–80.

    Google Scholar 

  13. Michalski, R.S. 1990. “Pattern Recognition as Rule-Guided Inductive Inference”. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-2, 4.

    Google Scholar 

  14. Robertson, S.E. and Sparck Jones, K. 1976. “Relevance Weighting of Search Terms”. Journal of the American Society for Information Science. Vol.27. pp.129–146.

    Google Scholar 

  15. Torgo, L. 1993. “Controlled Redundancy in Incremental Rule Learning”. ECML-93, pp.185–195.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Waterloo Waterloo, N2L 3G1, Ontario, Canada

    Aijun An & Nick Cercone

Authors
  1. Aijun An
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  2. Nick Cercone
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Editor information

Editors and Affiliations

  1. Institute of Computer Science, Polish Academy of Sciences, 01-237, Warsaw, Poland

    Zbigniew W. Raś

  2. Department of Computer Science, University of North Carolina, NC 28223, Charlotte, USA

    Zbigniew W. Raś

  3. Department of Science and Technology, Waseda University, 61-414, 3-4-1, Ohkubo, Shinjuku-ku, 169-8555, Tokyo, Japan

    Setsuo Ohsuga

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© 2000 Springer-Verlag Berlin Heidelberg

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An, A., Cercone, N. (2000). Rule Quality Measures Improve the Accuracy of Rule Induction: An Experimental Approach. In: Raś, Z.W., Ohsuga, S. (eds) Foundations of Intelligent Systems. ISMIS 2000. Lecture Notes in Computer Science(), vol 1932. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-39963-1_13

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  • DOI: https://doi.org/10.1007/3-540-39963-1_13

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41094-2

  • Online ISBN: 978-3-540-39963-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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