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Experimental Evaluation of Resampling Combined with Clustering and Random Oracle Using Genetic Fuzzy Systems

  • Tadeusz Lasota
  • Zbigniew Telec
  • Bogdan Trawiński
  • Grzegorz Trawiński
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 183)

Abstract

The ensemble methods combining resampling techniques: cross-validation, repeated holdout, and bootstrap sampling with clustering and random oracle using a genetic fuzzy rule-based system as a base learning algorithm were developed in Matlab environment. The methods were applied to the real-world regression problem of predicting the prices of residential premises based on historical data of sales/purchase transactions. The computationally intensive experiments were conducted aimed to compare the accuracy of ensembles generated by the proposed methods with different number of clusters or random oracle subsets. The statistical analysis of results was made employing nonparametric Friedman and Wilcoxon statistical tests.

Keywords

Mean Square Error Fuzzy System Random Oracle Training Instance Resampling Technique 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Breiman, L.: Bagging Predictors. Machine Learning 24(2), 123–140 (1996)MathSciNetMATHGoogle Scholar
  2. 2.
    Bühlmann, P., Yu, B.: Analyzing bagging. Annals of Statistics 30, 927–961 (2002)MathSciNetMATHCrossRefGoogle Scholar
  3. 3.
    Cordón, O., Gomide, F., Herrera, F., Hoffmann, F., Magdalena, L.: Ten years of genetic fuzzy systems: current framework and new trends. Fuzzy Sets and Systems 141, 5–31 (2004)MathSciNetMATHCrossRefGoogle Scholar
  4. 4.
    Cordón, O., Herrera, F.: A Two-Stage Evolutionary Process for Designing TSK Fuzzy Rule-Based Systems. IEEE Tr. on Sys., Man, and Cyb. -Part B 29(6), 703–715 (1999)CrossRefGoogle Scholar
  5. 5.
    Dunn, J.C.: Well separated clusters and optimal fuzzy partitions. Journal of Cybernetics 4(1), 95–104 (1974)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Graczyk, M., Lasota, T., Trawiński, B.: Comparative Analysis of Premises Valuation Models Using KEEL, RapidMiner, and WEKA. In: Nguyen, N.T., Kowalczyk, R., Chen, S.-M. (eds.) ICCCI 2009. LNCS(LNAI), vol. 5796, pp. 800–812. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  7. 7.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 2nd edn. Morgan Kauffman (2006)Google Scholar
  8. 8.
    Hartigan, J.A., Wong, M.A.: A K-Means Clustering Algorithm. Applied Statistics 28(1), 100–108 (1979)MATHCrossRefGoogle Scholar
  9. 9.
    Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edn. Springer (2009)Google Scholar
  10. 10.
    Kempa, O., Lasota, T., Telec, Z., Trawiński, B.: Investigation of Bagging Ensembles of Genetic Neural Networks and Fuzzy Systems for Real Estate Appraisal. In: Nguyen, N.T., Kim, C.-G., Janiak, A. (eds.) ACIIDS 2011, Part II. LNCS(LNAI), vol. 6592, pp. 323–332. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  11. 11.
    Król, D., Lasota, T., Trawiński, B., Trawiński, K.: Investigation of Evolutionary Optimization Methods of TSK Fuzzy Model for Real Estate Appraisal. International Journal of Hybrid Intelligent Systems 5(3), 111–128 (2008)MATHGoogle Scholar
  12. 12.
    Kuncheva, L.I., Rodríguez, J.J.: Classifier Ensembles with a Random Linear Oracle. IEEE Transactions on Knowledge and Data Engineering 19(4), 500–508 (2007)CrossRefGoogle Scholar
  13. 13.
    Kuncheva, L.I.: Switching between Selection and Fusion in Combining Classifiers: An Experiment. IEEE Trans. Systems, Man, and Cybernetics, Part B 32(2), 146–156 (2002)CrossRefGoogle Scholar
  14. 14.
    Lasota, T., Mazurkiewicz, J., Trawiński, B., Trawiński, K.: Comparison of Data Driven Models for the Validation of Residential Premises using KEEL. International Journal of Hybrid Intelligent Systems 7(1), 3–16 (2010)MATHGoogle Scholar
  15. 15.
    Lasota, T., Telec, Z., Trawiński, B., Trawiński, K.: Investigation of the eTS Evolving Fuzzy Systems Applied to Real Estate Appraisal. Journal of Multiple-Valued Logic and Soft Computing 17(2-3), 229–253 (2011)Google Scholar
  16. 16.
    Lasota, T., Telec, Z., Trawiński, G., Trawiński, B.: Empirical Comparison of Resampling Methods Using Genetic Fuzzy Systems for a Regression Problem. In: Yin, H., Wang, W., Rayward-Smith, V. (eds.) IDEAL 2011. LNCS, vol. 6936, pp. 17–24. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  17. 17.
    Lasota, T., Telec, Z., Trawiński, G., Trawiński, B.: Empirical Comparison of Resampling Methods Using Genetic Neural Networks for a Regression Problem. In: Corchado, E., Kurzyński, M., Woźniak, M. (eds.) HAIS 2011, Part II. LNCS(LNAI), vol. 6679, pp. 213–220. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  18. 18.
    Lughofer, E., Trawiński, B., Trawiński, K., Kempa, O., Lasota, T.: On Employing Fuzzy Modeling Algorithms for the Valuation of Residential Premises. Information Sciences 181, 5123–5142 (2011)CrossRefGoogle Scholar
  19. 19.
    Molinaro, A.N., Simon, R., Pfeiffer, R.M.: Prediction error estimation: a comparison of resampling methods. Bioinformatics 21(15), 3301–3307 (2005)CrossRefGoogle Scholar
  20. 20.
    Pakhira, M.K., Bandyopadhyay, S., Maulik, U.: Validity index for crisp and fuzzy clusters. Pattern Recognition 37(3), 487–501 (2004)MATHCrossRefGoogle Scholar
  21. 21.
    Pardo, C., Rodríguez, J.J., Díez-Pastor, J.F., García-Osorio, C.: Random Oracles for Regression Ensembles. In: Okun, O., Valentini, G., Re, M., et al. (eds.) Ensembles in Machine Learning Applications. SCI, vol. 373, pp. 181–199. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  22. 22.
    Woods, K., Kegelmeyer, W.P., Bowyer, K.: Combination of Multiple Classifiers Using Local Accuracy Estimates. IEEE Trans. Pattern Analysis and Machine Intelligence 19(4), 405–410 (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Tadeusz Lasota
    • 1
  • Zbigniew Telec
    • 2
  • Bogdan Trawiński
    • 2
  • Grzegorz Trawiński
    • 3
  1. 1.Department of Spatial ManagementWrocław University of Environmental and Life SciencesWrocławPoland
  2. 2.Institute of InformaticsWrocław University of TechnologyWrocławPoland
  3. 3.Faculty of ElectronicsWrocław University of TechnologyWrocławPoland

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