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A New Method of Improving Classification Accuracy of Decision Tree in Case of Incomplete Samples

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Artificial Intelligence and Soft Computing (ICAISC 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7894))

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Abstract

In the paper a new method is proposed which improves the classification accuracy of decision trees for samples with missing values. This aim was achieved by adding new nodes to the decision tree. The proposed procedure applies structures and functions of well-known C4.5 algorithm. However, it can be easily adapted to other methods, for forming decision trees. The efficiency of the new algorithm has been confirmed by tests using eleven databases from UCI Repository. The research has been concerned classification but the method is not limited to classification tasks.

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References

  1. Bartczuk, Ł., Rutkowska, D.: Type-2 fuzzy decision trees. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2008. LNCS (LNAI), vol. 5097, pp. 197–206. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  2. Bonissone, P., Cadenas, J.M., Carmen Garrido, M., Andrés Díaz-Valladares, R.: A fuzzy random forest (2010)

    Google Scholar 

  3. Breiman, L., Friedman, J., Olshen, R., Stone, C.: Classification and Regression Trees. Wadsworth Int. Group (1984)

    Google Scholar 

  4. Brodley, C.E., Utgoff, P.E.: Multivariate decision trees (1995)

    Google Scholar 

  5. Cpalka, K.: A method for designing flexible neuro-fuzzy systems. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds.) ICAISC 2006. LNCS (LNAI), vol. 4029, pp. 212–219. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  6. Cpałka, K.: On evolutionary designing and learning of flexible neuro-fuzzy structures for nonlinear classification. Nonlinear Analysis: Theory, Methods & Applications 71(12), 1659–1672 (2009)

    Article  Google Scholar 

  7. Gabryel, M., Scherer, R.: Determining fuzzy relation by evolutionary learning in neuro-fuzzy systems. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J. (eds.) Computational Intelligence: Methods and Applications, pp. 176–182. Academic Publishing House EXIT (2008)

    Google Scholar 

  8. Greblicki, W., Rutkowski, L.: Density-free bayes risk consistency of nonparametric pattern recognition procedures. Proceedings of the IEEE 69(4), 482–483 (1981)

    Article  Google Scholar 

  9. Hashemi, S., Yang, Y.: Flexible decision tree for data stream classification in the presence of concept change, noise and missing values. Data Mining and Knowledge Discovery 19, 95–131 (2009)

    Article  MathSciNet  Google Scholar 

  10. Haykin, S., Network, N.: A comprehensive foundation. Neural Networks 2 (2004)

    Google Scholar 

  11. Horzyk, A., Tadeusiewicz, R.: Self-optimizing neural networks. In: Yin, F.-L., Wang, J., Guo, C. (eds.) ISNN 2004. LNCS, vol. 3173, pp. 150–155. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  12. Korytkowski, M., Rutkowski, L., Scherer, R.: From ensemble of fuzzy classifiers to single fuzzy rule base classifier. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2008. LNCS (LNAI), vol. 5097, pp. 265–272. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  13. Korytkowski, M., Scherer, R., Rutkowski, L.: On combining backpropagation with boosting. In: 2006 International Joint Conference on Neural Networks, IEEE World Congress on Computational Intelligence, Vancouver, BC, Canada, pp. 1274–1277 (2006)

    Google Scholar 

  14. Mertz, C.J., Murphy, P.M.: UCI machine learning repository, http://archive.ics.uci.edu/ml/datasets.html

  15. Nowicki, R.: On combining neuro–fuzzy architectures with the rough set theory to solve classification problems with incomplete data. IEEE Trans. on Knowledge and Data Engineering 20(9), 1239–1253 (2008)

    Article  Google Scholar 

  16. Nowicki, R.: Rough–neuro–fuzzy structures for classification with missing data. IEEE Trans. on Systems, Man, and Cybernetics—Part B: Cybernetics 39(6), 1334–1347 (2009)

    Article  Google Scholar 

  17. Pawlak, Z.: Rough sets. International Journal of Computer and Information Sciences 11(5), 341–356 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  18. Przybył, A., Cpałka, K.: A new method to construct of interpretable models of dynamic systems. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2012, Part II. LNCS, vol. 7268, pp. 697–705. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  19. Qian, Y., Dang, C., Liang, J., Zhang, H., Ma, J.: On the evaluation of the decision performance of an incomplete decision table. Data & Knowledge Engineering 65(3), 373–400 (2008)

    Article  Google Scholar 

  20. Quinlan, J.: C4.5: Programs for Machine Learning. Morgan Kaufmann (1993)

    Google Scholar 

  21. Quinlan, J.R.: Improved use of continuous attributes in c4.5. Journal of Artificial Intelligence Research 4, 77–90 (1996)

    MATH  Google Scholar 

  22. Rutkowska, D., Nowicki, R.: Implication-based neuro–fuzzy architectures. International Journal of Applied Mathematics and Computer Science 10(4), 675–701 (2000)

    MATH  Google Scholar 

  23. Rutkowska, D., Rutkowski, L., Nowicki, R.: On processing of noisy data by fuzzy inference neural networks. In: Proceedings of the IASTED International Conference, Signal and Image Processing, Nassau, Bahamas, pp. 314–318 (October 1999)

    Google Scholar 

  24. Rutkowski, L.: Sequential estimates of probability densities by orthogonal series and their application in pattern classification. IEEE Transactions on Systems, Man and Cybernetics SMC-10(12), 918–920 (1980)

    MathSciNet  Google Scholar 

  25. Rutkowski, L.: Adaptive probabilistic neural networks for pattern classification in time-varying environment. IEEE Transactions on Neural Networks 15(4), 811–827 (2004)

    Article  MathSciNet  Google Scholar 

  26. Rutkowski, L.: Generalized regression neural networks in time-varying environment. IEEE Transactions on Neural Networks 15(3), 576–596 (2004)

    Article  Google Scholar 

  27. Rutkowski, L., Pietruczuk, L., Duda, P., Jaworski, M.: Decision trees for mining data streams based on the McDiarmid’s bound. IEEE Transactions on Knowledge and Data Engineering 25 (2013)

    Google Scholar 

  28. Scherer, R.: Boosting ensemble of relational neuro-fuzzy systems. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds.) ICAISC 2006. LNCS (LNAI), vol. 4029, pp. 306–313. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  29. Scherer, R., Korytkowski, M., Nowicki, R., Rutkowski, L.: Modular rough neuro-fuzzy systems for classification. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Wasniewski, J. (eds.) PPAM 2007. LNCS, vol. 4967, pp. 540–548. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  30. Scherer, R., Rutkowski, L.: A fuzzy relational system with linguistic antecedent certainty factors. In: Rutkowski, L., Kacprzyk, J. (eds.) Proceedings of the Sixth International Conference on Neural Network and Soft Computing. Advances in Soft Computing, pp. 563–569. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  31. Scherer, R., Rutkowski, L.: Neuro-fuzzy relational classifiers. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 376–380. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  32. Scherer, R., Rutkowski, L.: Connectionist fuzzy relational systems. In: Hagamuge, S.K., Wang, L. (eds.) Computational Intelligence for Modelling and Prediction. SCI, vol. 2, pp. 35–47. Springer, Heidelberg (2005)

    Google Scholar 

  33. Starczewski, J.T.: On defuzzification of interval type-2 fuzzy sets. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2008. LNCS (LNAI), vol. 5097, pp. 333–340. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  34. Starczewski, J.T.: A type-1 approximation of interval type-2 FLS. In: Di Gesù, V., Pal, S.K., Petrosino, A. (eds.) WILF 2009. LNCS, vol. 5571, pp. 287–294. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  35. Starczewski, J.T.: General type-2 fls with uncertainty generated by fuzzy rough sets. In: FUZZ-IEEE, pp. 1–6 (2010)

    Google Scholar 

  36. Zhang, S.: Decision tree classifiers sensitive to heterogeneous costs. Journal of Systems and Software 85(4), 771–779 (2012)

    Article  Google Scholar 

  37. Zhang, S., Qin, Z., Ling, C., Sheng, S.: “Missing is useful”: missing values in cost-sensitive decision trees. IEEE Transactions on Knowledge and Data Engineering 17(12), 1689–1693 (2005)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Institute of Computational Intelligence, Czestochowa University of Technology, Al. Armii Krajowej 36, 42-200, Czestochowa, Poland

    Bartosz A. Nowak, Robert K. Nowicki & Wojciech K. Mleczko

Authors
  1. Bartosz A. Nowak
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  2. Robert K. Nowicki
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  3. Wojciech K. Mleczko
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Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Armii Krajowej 36, 42-200, Częstochowa, Poland

    Leszek Rutkowski , Marcin Korytkowski  & Rafał Scherer ,  & 

  2. AGH University of Science and Technology, Michiewicza 30, 30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  3. Department of Electrical Engineering and Computer Sciences, University of California, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  4. Electrical and Computer Engineering, University of Louisville, 405 Lutz Hall, 40292, Louisville, KY, USA

    Jacek M. Zurada

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Nowak, B.A., Nowicki, R.K., Mleczko, W.K. (2013). A New Method of Improving Classification Accuracy of Decision Tree in Case of Incomplete Samples. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2013. Lecture Notes in Computer Science(), vol 7894. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38658-9_40

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  • DOI: https://doi.org/10.1007/978-3-642-38658-9_40

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38657-2

  • Online ISBN: 978-3-642-38658-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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