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Ibero-American Conference on Artificial Intelligence

Brazilian Symposium on Artificial Intelligence

IBERAMIA 2000, SBIA 2000: Advances in Artificial Intelligence pp 1–10Cite as

Decision-Rule Solutions for Data Mining with Missing Values

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 1952)

Abstract

A method is presented to induce decision rules from data with missing values where (a) the format of the rules is no different than rules for data without missing values and (b) no special features are spe- cified to prepare the the original data or to apply the induced rules. This method generates compact Disjunctive Normal Form (DNF) rules. Each class has an equal number of unweighted rules. A new example is classi- fied by applying all rules and assigning the example to the class with the most satisfied rules. Disjuncts in rules are naturally overlapping. When combined with voted solutions, the inherent redundancy is enhanced. We provide experimental evidence that this transparent approach to classi- fication can yield strong results for data mining with missing values.

Keywords

  • decision rule induction
  • boosting

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  • DOI: 10.1007/3-540-44399-1_1
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References

  1. E. Bauer and R. Kohavi. An empirical comparison of voting classification algorithms: Bagging, boosting and variants. Machine Learning, 36(1): 105–139, 1999.

    CrossRef  Google Scholar 

  2. C. Blake, E. Keogh, and C. Merz. Uci repository of machine learning databases. Technical report, University of California Irvine, 1999. http://www.ics.uci.edu/~mlearn/MLRepository.html

  3. L. Breiman. Bagging predictors. Machine Learning, 24:123–140, 1996.

    MATH  MathSciNet  Google Scholar 

  4. L. Breiman, J. Friedman, R. Olshen, and C. Stone. Classification and Regression Trees. Wadsworth, Monterrey, CA., 1984.

    Google Scholar 

  5. W. Cohen. Fast effective rule induction. In Proceedings of the Twelfth International Conference on Machine Learning, pages 115–123, 1995.

    Google Scholar 

  6. J. Friedman, T. Hastie, and R. Tibshirani. Additive logistic regression: A statistical view of boosting. Technical report, Stanford University Statistics Department, 1998. http://www.stat-stanford.edu/~tibs

  7. D. Pyle. Data Preparation for Data Mining. Morgan Kaufmann, San Francisco, 1999.

    Google Scholar 

  8. J. Quinlan. Unknown attribute values in induction. In International Workshop on Machine Learning, pages 164–168, Ithica, NY, 1989.

    Google Scholar 

  9. R. Schapire. A brief introduction to boosting. In Proceedings of International Joint Conference on Artificial Intelligence, pages 1401–1405, 1999.

    Google Scholar 

  10. S. Weiss, C. Apté, F. Damerau, and et al. Maximizing text-mining performance. tiIEEE Intelligent Systems, 14(4): 63–69, 1999.

    Google Scholar 

  11. S. Weiss and N. Indurkhya. Optimized rule induction. IEEE EXPERT, 8(6): 61–69, December 1993.

    CrossRef  Google Scholar 

  12. S. Weiss and N. Indurkhya. Predictive Data Mining: A Practical Guide. Morgan Kaufmann, 1998. DMSK Software: http://www.data-miner.com

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Author information

Authors and Affiliations

  1. IBM T.J. Watson Research Center, P.O. Box 218, Yorktown Heights, NY 10598, USA

    Sholom M. Weiss & Nitin Indurkhya

Authors
  1. Sholom M. Weiss
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  2. Nitin Indurkhya
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Statistics Computational Intelligence Laboratory, University of São Paulo, Avenida Trabalhador Sãocarlense 400, 13566-590, São Carlos, Brazil

    Maria Carolina Monard

  2. Computer Engineering Department Intelligent Techniques Laboratory, University of São Paulo, Av. Prof. Luciano Gualberto, 158, tv. 3, 05508-900, São Paulo, Brazil

    Jaime Simão Sichman

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© 2000 Springer-Verlag Berlin Heidelberg

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Weiss, S.M., Indurkhya, N. (2000). Decision-Rule Solutions for Data Mining with Missing Values. In: Monard, M.C., Sichman, J.S. (eds) Advances in Artificial Intelligence. IBERAMIA SBIA 2000 2000. Lecture Notes in Computer Science(), vol 1952. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44399-1_1

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  • DOI: https://doi.org/10.1007/3-540-44399-1_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41276-2

  • Online ISBN: 978-3-540-44399-5

  • eBook Packages: Springer Book Archive