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Handling Incomplete Categorical Data for Supervised Learning

  • Conference paper
Advances in Applied Artificial Intelligence (IEA/AIE 2006)

Abstract

Classification is an important research topic in knowledge discovery. Most of the researches on classification concern that a complete dataset is given as a training dataset and the test data contain all values of attributes without missing. Unfortunately, incomplete data usually exist in real-world applications. In this paper, we propose new handling schemes of learning classification models from incomplete categorical data. Three methods based on rough set theory are developed and discussed for handling incomplete training data. The experiments were made and the results were compared with previous methods making use of a few famous classification models to evaluate the performance of the proposed handling schemes.

This work was supported in part by the National Science Council of Taiwan, R. O. C., under contract NSC94-2213-E-024-004.

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References

  1. Blake, C., Keogh, E., Merz, C.J.: UCI repository of machine learning database. Irvine, University of California, Department of Information and Computer Science (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html

  2. Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines, Software (2001), available at: http://www.csie.ntu.edu.tw/~cjlin/libsvm

  3. Chien, B.C., Lin, J.Y., Yang, W.P.: Learning effective classifiers with z-value measure based on genetic programming. Pattern Recognition 37, 1957–1972 (2004)

    Article  MATH  Google Scholar 

  4. Chien, B.C., Yang, J.H., Lin, W.Y.: Generating effective classifiers with supervised learning of genetic programming. In: Proceedings of the 5th International Conference on Data Warehousing and Knowledge Discovery, pp. 192–201 (2003)

    Google Scholar 

  5. Dempster, P., Laird, N.M., Rubin, D.B.: Maximum-likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society B39, 1–38 (1977)

    MathSciNet  Google Scholar 

  6. Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis. Wiley, John and Sons Incorporated Publishers, New York (1973)

    MATH  Google Scholar 

  7. Friedman, J.H.: A recursive partitioning decision rule for non-parametric classification. IEEE Transactions on Computer Science, 404–408 (1977)

    Google Scholar 

  8. Grzymala-Busse, J.W.: On the unknown attribute values in learning from examples. In: RaÅ›, Z.W., Zemankova, M. (eds.) ISMIS 1991. LNCS, vol. 542, pp. 368–377. Springer, Heidelberg (1991)

    Chapter  Google Scholar 

  9. Grzymala-Busse, J.W., Hu, M.: A comparison of several approaches to missing attribute values in data mining. In: Ziarko, W.P., Yao, Y. (eds.) RSCTC 2000. LNCS, vol. 2005, pp. 378–385. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  10. Grzymala-Busse, J.W.: Rough set strategies to data with missing attribute values. In: Proceedings of the Workshop on Foundations and New Directions in Data Mining, associated with the third IEEE International Conference on Data Mining, pp. 56–63 (2003)

    Google Scholar 

  11. Gunn, S.R.: Support vector machines for classification and regression. Technical Report, School of Electronics and Computer Science University of Southampton (1998)

    Google Scholar 

  12. Han, J., Kamber, M.: Data Mining: Concept and Techniques. Morgan Kaufmann, San Francisco (2001)

    Google Scholar 

  13. Hathaway, R.J., Bezdek, J.C.: Fuzzy c-means clustering of incomplete data. IEEE Transactions on Systems, Man, and Cybernetics-part B: Cybernetics 31(5) (2001)

    Google Scholar 

  14. Hong, T.P., Tseng, L.H., Chien, B.C.: Learning fuzzy rules from incomplete numerical data by rough sets. In: Proceedings of the 2002 IEEE International Conference on Fuzzy Systems, pp. 1438–1443 (2002)

    Google Scholar 

  15. Hong, T.P., Tseng, L.H., Wang, S.-L.: Learning rules from incomplete training examples by rough sets. Expert Systems with Applications 22, 285–293 (2002)

    Article  Google Scholar 

  16. Kohavi, R.: Scaling up the accuracy of naïve-bayes classifiers: a decision-tree hybrid. In: Knowledge Discovery & Data Mining, pp. 202–207. AAAI Press/MIT Press, Cambridge/Menlo Park (1996)

    Google Scholar 

  17. Koninenko, I., Bratko, K., Roskar, E.: Experiments in automatic learning of medical diagnostic rules. Technical Report, Jozef Stenfan Institute, Ljubljana (1984)

    Google Scholar 

  18. Kryszkiewicz, M.: Rough set approach to incomplete information systems. Information Science 112, 39–49 (1998)

    Article  MATH  MathSciNet  Google Scholar 

  19. Pawlak, Z.: Rough sets. International Journal of Computer and Information Sciences 11, 341–356 (1982)

    Article  MATH  MathSciNet  Google Scholar 

  20. Pawlak, Z., Skowron, A.: Rough membership functions. In: Yager, R.R., Fedrizzi, M., Kacprzyk, J. (eds.) Advances in the Dempster-Shafer Theory of Evidence, pp. 251–271 (1994)

    Google Scholar 

  21. Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo (1993)

    Google Scholar 

  22. Singleton, A.: Genetic Programming with C++. Byte, pp. 171–176 (1994), http://www.byte.com/art/9402/sec10/ar-t1.htm

  23. Slowinski, R., Stefanowski, J.: Handling various types of uncertainty in the rough set approach. In: Proceedings of the International Workshop on Rough Sets and Knowledge Discovery, pp. 366–376 (1993)

    Google Scholar 

  24. Stefanowski, J., Tsoukias, A.: On the extension of rough sets under incomplete information. In: Zhong, N., Skowron, A., Ohsuga, S. (eds.) RSFDGrC 1999. LNCS, vol. 1711, pp. 73–82. Springer, Heidelberg (1999)

    Chapter  Google Scholar 

  25. Witten, H., Frank, E.: Data Mining: Practical machine learning tools with Java implementations. Morgan Kaufmann, San Francisco (2000)

    Google Scholar 

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Chien, BC., Lu, CF., Hsu, S.J. (2006). Handling Incomplete Categorical Data for Supervised Learning. In: Ali, M., Dapoigny, R. (eds) Advances in Applied Artificial Intelligence. IEA/AIE 2006. Lecture Notes in Computer Science(), vol 4031. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11779568_139

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  • DOI: https://doi.org/10.1007/11779568_139

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-35453-6

  • Online ISBN: 978-3-540-35454-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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