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GEP-Induced Expression Trees as Weak Classifiers

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Book cover Advances in Data Mining. Medical Applications, E-Commerce, Marketing, and Theoretical Aspects (ICDM 2008)

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Abstract

The paper proposes applying Gene Expression Programming (GEP) to induce expression trees used subsequently as weak classifiers. Two techniques of constructing ensemble classifiers from weak classifiers are investigated in the paper. The working hypothesis of the paper can be stated as follows: given a set of classifiers generated through applying gene expression programming method and using some variants of boosting technique, one can construct the ensemble producing effectively high quality classification results. A detailed description of the proposed GEP implementation generating classifiers in the form of expression trees is followed by the report on AdaBoost and boosting algorithms used to construct an ensemble classifier. To validate the approach computational experiment involving several benchmark datasets has been carried out. Experiment results show that using GEP-induced expression trees as weak classifiers allows for construction of a high quality ensemble classifier outperforming, in terms of classification accuracy, many other recently published solutions.

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References

  1. Asuncion, A., Newman, D.J.: UCI Machine Learning Repository, University of California, School of Information and Computer Science (2007), http://www.ics.uci.edu/~mlearn/MLRepository.html

  2. Battiti, R., Colla, A.M.: Democracy in neural nets: Voting schemes for classification. Neural Networks 7(4), 691–707 (1994)

    Article  Google Scholar 

  3. Bauer, E., Kohavi, R.: An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning 36(1-2), 105–139 (1999)

    Article  Google Scholar 

  4. Bennett, P.N.: Building Reliable Metaclassifiers for Text Learning, Ph.D. Thesis, School of Computer Science, Carnegie Mellon University, Pittsburgh (2006)

    Google Scholar 

  5. Dasarathy, B.V., Sheela, B.V.: Composite classifier system design: Concepts and Mathodology. Proceedings of the IEEE 67(5), 708–713 (1979)

    Article  Google Scholar 

  6. Drucker, H., Cortes, C., Jackel, L.D., LeCun, Y., Vapnik, V.: Boosting and other ensemble methods. Neural Computation 6(6), 1289–1301 (1994)

    Article  MATH  Google Scholar 

  7. Duan, L., Tang, C., Zhang, T., Wei, D., Zhang, H.: Distance Guided Classification with Gene Expression Programming. In: Li, X., Zaïane, O.R., Li, Z. (eds.) ADMA 2006. LNCS (LNAI), vol. 4093, pp. 239–246. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  8. Duda, R., Hart, P., Stork, D.: Pattern Classification. John Wiley & Sons, Inc., New York (2001)

    MATH  Google Scholar 

  9. Ferreira, C.: Gene Expression Programming: a New Adaptive Algorithm for Solving Problems. Complex Systems 13(2), 87–129 (2001)

    MathSciNet  Google Scholar 

  10. Ferreira, C.: Gene Expression Programming. Studies in Computational Intelligence 21, 337–380 (2006)

    Article  Google Scholar 

  11. Freund, Y., Schapire, R.E.: Decision-theoretic generalization of on-line learning and application to boosting. Journal of Computer and System Science 55(1), 119–139 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  12. Gama, J.: Local cascade generalization. In: Proceedings of the 15th International Conference on Machine Learning, pp. 206–214 (1998)

    Google Scholar 

  13. Ho, T.K., Hull, J.J., Srihari, S.N.: Decision Combination in Multiple Classifier Systems. IEEE Transactions on Pattern Recognition and Machine Intelligence 16(1), 66–75 (1994)

    Article  Google Scholar 

  14. Huang, C.-L., Chen, M.-C., Wang, C.-J.: Credit scoring with a data mining approach based on support vector machines. Expert Systems with Applications 33, 847–856 (2007)

    Article  Google Scholar 

  15. Karakasis, V.K., Stafylopatis, A.: Data Mining based on Gene Expression Programming and Clonal Selection. In: Proc. IEEE Congress on Evolutionary Computation, pp. 514–521 (2006)

    Google Scholar 

  16. Kuncheva, L.I.: Classifier ensembles for changing environments. In: Roli, F., Kittler, J., Windeatt, T. (eds.) MCS 2004. LNCS, vol. 3077, pp. 1–15. Springer, Heidelberg (2004)

    Google Scholar 

  17. Lam, L., Suen, C.Y.: Optimal combination of pattern classifiers. Pattern Recognition Letters 16(9), 945–954 (1995)

    Article  Google Scholar 

  18. Last, M., Maimon, O.: A Compact and Accurate Model for Classification. IEEE Transactions on Knowledge and Data Engineering 16(2), 203–215 (2004)

    Article  Google Scholar 

  19. Li, X., Zhou, C., Xiao, W., Nelson, P.C.: Prefix Gene Expression Programming. In: Proc. Genetic and Evolutionary Computation Conference, Washington, pp. 25–31 (2005)

    Google Scholar 

  20. Pena Centeno, T., Lawrence, N.D.: Optimising Kernel Parameters and Regularisation Coefficients for Non-linear Discriminant Analysis. Journal of Machine Learning Research 7, 455–491 (2006)

    MathSciNet  Google Scholar 

  21. Polikar, R.: Ensemble Based Systems in Decision Making. IEEE Circuits and Systems Magazine 3, 22–43 (2006)

    Google Scholar 

  22. Schapire, R.E., Freund, Y., Bartlett, P., Lee, W.S.: Boosting the margin: A new explanation for the effectiveness of voting methods. The Annals of Statistics 26(5), 1651–1686 (1998)

    Article  MATH  MathSciNet  Google Scholar 

  23. Srinivasa, K.B., Singh, A., Thomas, A.O., Venugopal, K.R., Patnoik, L.M.: Generic Feature Extraction for Classification Using Fuzzy C-Means Clustering. In: Proc. Intelligent Sensing and Information Processing Conference, pp. 33–38 (2005)

    Google Scholar 

  24. Torre, F.: Boosting Correct Least General Generalizations, Technical Report GRAppA-0104, Grenoble (2004)

    Google Scholar 

  25. Wang, W., Li, Q., Han, S., Lin, H.: A Preliminary Study on Constructing Decision Tree with Gene Expression Programming. In: Proc. First International Conference on Innovative Computing, Information and Control, vol. 1, pp. 222–225 (2006)

    Google Scholar 

  26. Weinert, W.R., Lopes, H.S.: GEPCLASS: A Classification Rule Discovery Tool Using Gene Expression Programming. In: Li, X., Zaïane, O.R., Li, Z. (eds.) ADMA 2006. LNCS (LNAI), vol. 4093, pp. 871–880. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  27. Wolpert, D.H.: Stacked generalization. Neural Networks 5, 241–259 (1992)

    Article  Google Scholar 

  28. Statlog Datasets: comparison of results (accessed on 27 December 2007), www.is.umk.pl/projects/datasets.html#Cleveland

  29. Zeng, T., Xiang, Y., Chen, P., Liu, Y.: A Model of Immune Gene Expression Programming for Rule Mining. Journal of Universal Computer Science 13(7), 1239–1252 (2007)

    Google Scholar 

  30. Zhou, C., Xiao, W., Tirpak, T.M., Nelson, P.C.: Evolving Accurate and Compact Classification Rules with Gene Expression Programming. IEEE Transactions on Evolutionary Computation 7(6), 519–531 (2003)

    Article  Google Scholar 

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Authors and Affiliations

  1. Institute of Computer Science, Gdańsk University, Wita Stwosza 57, 80-952, Gdańsk, Poland

    Joanna Jȩdrzejowicz

  2. Department of Information Systems, Gdynia Maritime University, Morska 83, 81-225, Gdynia, Poland

    Piotr Jȩdrzejowicz

Authors
  1. Joanna Jȩdrzejowicz
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  2. Piotr Jȩdrzejowicz
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Petra Perner

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Jȩdrzejowicz, J., Jȩdrzejowicz, P. (2008). GEP-Induced Expression Trees as Weak Classifiers. In: Perner, P. (eds) Advances in Data Mining. Medical Applications, E-Commerce, Marketing, and Theoretical Aspects. ICDM 2008. Lecture Notes in Computer Science(), vol 5077. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70720-2_10

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  • DOI: https://doi.org/10.1007/978-3-540-70720-2_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-70717-2

  • Online ISBN: 978-3-540-70720-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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