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Investigation of Rotation Forest Ensemble Method Using Genetic Fuzzy Systems for a Regression Problem

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Intelligent Information and Database Systems (ACIIDS 2012)

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Abstract

The rotation forest ensemble method using a genetic fuzzy rule-based system as a base learning algorithm was developed in Matlab environment. The method was 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 our proposed method with bagging, repeated holdout, and repeated cross-validation models. The statistical analysis of results was made employing nonparametric Friedman and Wilcoxon statistical tests.

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References

  1. Breiman, L.: Bagging Predictors. Machine Learning 24(2), 123–140 (1996)

    MATH  Google Scholar 

  2. Breiman, L.: Random Forests. Machine Learning 45(1), 5–32 (2001)

    Article  MATH  Google Scholar 

  3. Bryll, R.: Attribute bagging: improving accuracy of classifier ensembles by using random feature subsets. Pattern Recognition 20(6), 1291–1302 (2003)

    Article  MATH  Google Scholar 

  4. Bühlmann, P., Yu, B.: Analyzing bagging. Annals of Statistics 30, 927–961 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  5. 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)

    Article  MathSciNet  MATH  Google Scholar 

  6. Cordón, O., Herrera, F.: A Two-Stage Evolutionary Process for Designing TSK Fuzzy Rule-Based Systems. IEEE Trans. Sys., Man, and Cyb.-Part B 29(6), 703–715 (1999)

    Article  Google Scholar 

  7. Fumera, G., Roli, F., Serrau, A.: A theoretical analysis of bagging as a linear combination of classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(7), 1293–1299 (2008)

    Article  Google Scholar 

  8. Gashler, M., Giraud-Carrier, C., Martinez, T.: Decision Tree Ensemble: Small Heterogeneous Is Better Than Large Homogeneous. In: Seventh International Conference on Machine Learning and Applications, ICMLA 2008, pp. 900–905 (2008)

    Google Scholar 

  9. Ho, T.K.: The Random Subspace Method for Constructing Decision Forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(8), 832–844 (1998)

    Article  Google Scholar 

  10. Jędrzejowicz, J., Jędrzejowicz, P.: Rotation Forest with GEP-Induced Expression Trees. In: O’Shea, J., Nguyen, N.T., Crockett, K., Howlett, R.J., Jain, L.C. (eds.) KES-AMSTA 2011. LNCS (LNAI), vol. 6682, pp. 495–503. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  11. 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. LNAI (LNCS), vol. 6592, pp. 323–332. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  12. Kotsiantis, S.: Combining bagging, boosting, rotation forest and random subspace methods. Artificial Intelligence Review 35(3), 223–240 (2011)

    Article  Google Scholar 

  13. Kotsiantis, S.B., Pintelas, P.E.: Local Rotation Forest of Decision Stumps for Regression Problems. In: 2nd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2009, pp. 170–174 (2009)

    Google Scholar 

  14. 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)

    Article  MATH  Google Scholar 

  15. Kuncheva, L.I., Rodríguez, J.J.: An Experimental Study on Rotation Forest Ensembles. In: Haindl, M., Kittler, J., Roli, F. (eds.) MCS 2007. LNCS, vol. 4472, pp. 459–468. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  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)

    Chapter  Google Scholar 

  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, vol. 6679, pp. 213–220. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  18. Rodrígeuz, J.J., Kuncheva, I., Alonso, C.J.: Rotation forest: A new classifier ensemble method. IEEE Trans. on Pattern Analysis and Mach. Intel. 28(10), 1619–1630 (2006)

    Article  Google Scholar 

  19. Schapire, R.E.: The strength of weak learnability. Mach. Learning 5(2), 197–227 (1990)

    Google Scholar 

  20. Wolpert, D.H.: Stacked Generalization. Neural Networks 5(2), 241–259 (1992)

    Article  Google Scholar 

  21. Zhang, C.-X., Zhang, J.-S.: A variant of Rotation Forest for constructing ensemble classifiers. Pattern Analysis & Applications 13(1), 59–77 (2010)

    Article  MathSciNet  Google Scholar 

  22. Zhang, C.-X., Zhang, J.-S.: RotBoost: A technique for combining Rotation Forest and AdaBoost. Pattern Recognition Letters 29(10), 1524–1536 (2008)

    Article  Google Scholar 

  23. Zhang, C.-X., Zhang, J.-S., Wang, G.-W.: An empirical study of using Rotation Forest to improve regressors. Applied Mathematics and Computation 195(2), 618–629 (2008)

    Article  MathSciNet  MATH  Google Scholar 

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Lasota, T., Telec, Z., Trawiński, B., Trawiński, G. (2012). Investigation of Rotation Forest Ensemble Method Using Genetic Fuzzy Systems for a Regression Problem. In: Pan, JS., Chen, SM., Nguyen, N.T. (eds) Intelligent Information and Database Systems. ACIIDS 2012. Lecture Notes in Computer Science(), vol 7196. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28487-8_41

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  • DOI: https://doi.org/10.1007/978-3-642-28487-8_41

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28486-1

  • Online ISBN: 978-3-642-28487-8

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