CLUB-DRF: A Clustering Approach to Extreme Pruning of Random Forests

  • Khaled Fawagreh
  • Mohamed Medhat Gaber
  • Eyad Elyan
Conference paper


Random Forest (RF) is an ensemble supervised machine learning technique that was developed by Breiman over a decade ago. Compared with other ensemble techniques, it has proved its superiority. Many researchers, however, believe that there is still room for enhancing and improving its performance accuracy. This explains why, over the past decade, there have been many extensions of RF where each extension employed a variety of techniques and strategies to improve certain aspect(s) of RF. Since it has been proven empirically that ensembles tend to yield better results when there is a significant diversity among the constituent models, the objective of this paper is twofold. First, it investigates how data clustering (a well known diversity technique) can be applied to identify groups of similar decision trees in an RF in order to eliminate redundant trees by selecting a representative from each group (cluster). Second, these likely diverse representatives are then used to produce an extension of RF termed CLUB-DRF that is much smaller in size than RF, and yet performs at least as good as RF, and mostly exhibits higher performance in terms of accuracy. The latter refers to a known technique called ensemble pruning. Experimental results on 15 real datasets from the UCI repository prove the superiority of our proposed extension over the traditional RF. Most of our experiments achieved at least 92 % or above pruning level while retaining or outperforming the RF accuracy.


Random Forest Class Label Training Instance Multiclass Classification Neural Network Ensemble 
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.


  1. 1.
    Adeva, J.J.G., Beresi, U., Calvo, R.: Accuracy and diversity in ensembles of text categorisers. CLEI Electron. J. 9(1) (2005)Google Scholar
  2. 2.
    Amit, Y., Geman, D.: Shape quantization and recognition with randomized trees. Neural Comput. 9(7), 1545–1588 (1997)CrossRefGoogle Scholar
  3. 3.
    Bache, K., Lichman, M.: Uci Machine Learning Repository. University of California, Irvine (2013)Google Scholar
  4. 4.
    Bakker, B., Heskes, T.: Clustering ensembles of neural network models. Neural Netw. 16(2), 261–269 (2003)CrossRefGoogle Scholar
  5. 5.
    Bernard, S., Heutte, L., Adam, S.: On the selection of decision trees in random forests. In: International Joint Conference on Neural Networks. IJCNN 2009, pp. 302–307. June 2009Google Scholar
  6. 6.
    Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996)zbMATHGoogle Scholar
  7. 7.
    Breiman, L.: Stacked regressions. Mach. Learn. 24(1), 49–64 (1996)zbMATHGoogle Scholar
  8. 8.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)zbMATHCrossRefGoogle Scholar
  9. 9.
    Brown, G., Wyatt, J., Harris, R., Yao, X.: Diversity creation methods: a survey and categorisation. Inf. Fusion 6(1), 5–20 (2005)CrossRefGoogle Scholar
  10. 10.
    Brown, R.D., Martin, Y.C.: An evaluation of structural descriptors and clustering methods for use in diversity selection. SAR QSAR Environ. Res. 8(1–2), 23–39 (1998)CrossRefGoogle Scholar
  11. 11.
    Diao, R., Chao, F., Peng, T., Snooke, N., Shen, Q.: Feature selection inspired classifier ensemble reduction. Cybern. IEEE Trans. 44(8), 1259–1268 (2014)CrossRefGoogle Scholar
  12. 12.
    Ester, M., Kriegel, H.-P., Sander, J., Xiaowei, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. KDD 96, 226–231 (1996)Google Scholar
  13. 13.
    Fleiss, J.L., Levin, B., Cho Paik, M.: Statistical Methods for Rates and Proportions. Wiley, New York (2013)zbMATHGoogle Scholar
  14. 14.
    Freund, Y., Robert, E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997)MathSciNetzbMATHCrossRefGoogle Scholar
  15. 15.
    Giacinto, G., Roli, F.: Design of effective neural network ensembles for image classification purposes. Image Vis. Comput. 19(9), 699–707 (2001)CrossRefGoogle Scholar
  16. 16.
    Giacinto, G., Roli, F., Fumera, G.: Design of effective multiple classifier systems by clustering of classifiers. In: Proceedings of 15th International Conference on Pattern Recognition, vol. 2, pp. 160–163. IEEE (2000)Google Scholar
  17. 17.
    Guha, S., Rastogi, R., Shim, K.: Cure: an efficient clustering algorithm for large databases. In: ACM SIGMOD Record, vol. 27, pp. 73–84. ACM (1998)Google Scholar
  18. 18.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA Data Mining Software: An Update, vol. 11. ACM, New York (2009)Google Scholar
  19. 19.
    Ho, T.H: Random decision forests. In: Proceedings of the Third International Conference on Document Analysis and Recognition, vol. 1, pp. 278–282. IEEE (1995)Google Scholar
  20. 20.
    Ho, T.K.: The random subspace method for constructing decision forests. Pattern Anal. Mach. Intell. IEEE Trans. 20(8), 832–844 (1998)CrossRefGoogle Scholar
  21. 21.
    Huang, Z.: Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Min. Knowl. Discov. 2(3), 283–304 (1998)CrossRefGoogle Scholar
  22. 22.
    Huang, Z., Ng, M.K.: A fuzzy k-modes algorithm for clustering categorical data. Fuzzy Syst. IEEE Trans. 7(4), 446–452 (1999)CrossRefGoogle Scholar
  23. 23.
    Jain, A.K.: Data clustering: 50 years beyond k-means. Pattern Recognit. Lett. 31(8), 651–666 (2010)CrossRefGoogle Scholar
  24. 24.
    Kohavi, R., et al.: A study of cross-validation and bootstrap for accuracy estimation and model selection. IJCAI 14, 1137–1145 (1995)Google Scholar
  25. 25.
    Kohavi, R., Wolpert, D.H., et al.: Bias plus variance decomposition for zero-one loss functions. In: ICML, pp. 275–283 (1996)Google Scholar
  26. 26.
    Kulkarni, V.Y., Sinha, P.K.: Pruning of random forest classifiers: a survey and future directions. In: International Conference on Data Science Engineering (ICDSE), pp. 64–68, July 2012Google Scholar
  27. 27.
    Kuncheva, L.I., Hadjitodorov, S.T.: Using diversity in cluster ensembles. In: IEEE International Conference on Systems, Man and Cybernetics, vol. 2, pp. 1214–1219. IEEE (2004)Google Scholar
  28. 28.
    Kuncheva, L.I., Whitaker, C.J.: Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Mach. Learn. 51(2), 181–207 (2003)zbMATHCrossRefGoogle Scholar
  29. 29.
    Lazarevic, A., Obradovic, Z.: Effective pruning of neural network classifier ensembles. In: Proceedings of International Joint Conference on Neural Networks. IJCNN’01, vol. 2, pp. 796–801. IEEE (2001)Google Scholar
  30. 30.
    Lee, J., Sun, Y., Nabar, R., Lou, H.-L.: Cluster-based transmit diversity scheme for mimo ofdm systems. In: IEEE 68th Vehicular Technology Conference, VTC 2008-Fall, pp. 1–5. IEEE (2008)Google Scholar
  31. 31.
    Leo, B., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and regression trees. Wadsworth Int. Group (1984)Google Scholar
  32. 32.
    Li, J., Yi, Ke., Zhang, Q.: Clustering with diversity. In: Automata, Languages and Programming, pp. 188–200. Springer (2010)Google Scholar
  33. 33.
    Maclin, R., Opitz, D.: Popular ensemble methods: an empirical study. J. Artif. Intell. Res. 11(1–2), 169–198 (1999)zbMATHGoogle Scholar
  34. 34.
    MacQueen, J., et al.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, p. 14. California (1967)Google Scholar
  35. 35.
    Mardia, K.V., Kent, J.T., Bibby, J.M.: Multivariate analysis (1980)Google Scholar
  36. 36.
    Ng, R.T., Han, J.: Clarans: a method for clustering objects for spatial data mining. Knowl. Data Eng. IEEE Trans. 14(5), 1003–1016 (2002)CrossRefGoogle Scholar
  37. 37.
    Pakhira, M.K.: A modified k-means algorithm to avoid empty clusters. Int. J. Recent Trends Eng. 1(1), 1 (2009)Google Scholar
  38. 38.
    Partridge, D., Krzanowski, W.: Software diversity: practical statistics for its measurement and exploitation. Inf. Softw. Technol. 39(10), 707–717 (1997)CrossRefGoogle Scholar
  39. 39.
    Polikar, R.: Ensemble based systems in decision making. Circuits Syst. Mag. IEEE 6(3), 21–45 (2006)CrossRefGoogle Scholar
  40. 40.
    Qiang, F., Shang-Xu, H., Sheng-Ying, Z.: Clustering-based selective neural network ensemble. J. Zhejiang Univ. Sci. A 6(5), 387–392 (2005)Google Scholar
  41. 41.
    Rokach, L.: Ensemble-based classifiers. Artif. Intell. Rev. 33(1–2), 1–39 (2010)CrossRefGoogle Scholar
  42. 42.
    San, O.M., Huynh, V.-N., Nakamori, Y.: An alternative extension of the k-means algorithm for clustering categorical data. Int. J. Appl. Math. Comput. Sci. 14(2), 241–248 (2004)MathSciNetzbMATHGoogle Scholar
  43. 43.
    Sharpton, T., Jospin, G., Wu, D., Langille, M., Pollard, K., Eisen, J.: Sifting through genomes with iterative-sequence clustering produces a large, phylogenetically diverse protein-family resource. BMC Bioinform. 13(1), 264 (2012)CrossRefGoogle Scholar
  44. 44.
    Shemetulskis, N.E., Dunbar Jr, J.B., Dunbar, B.W., Moreland, D.W., Humblet, C.: Enhancing the diversity of a corporate database using chemical database clustering and analysis. J. Comput.-Aided Mol. Des. 9(5), 407–416 (1995)CrossRefGoogle Scholar
  45. 45.
    Skalak, D.B.: The sources of increased accuracy for two proposed boosting algorithms. In: Proceedings of American Association for Artificial Intelligence, AAAI-96, Integrating Multiple Learned Models Workshop, vol. 1129, p. 1133. Citeseer (1996)Google Scholar
  46. 46.
    Smyth, P., Wolpert, D.: Linearly combining density estimators via stacking. Mach. Learn. 36(1–2), 59–83 (1999)CrossRefGoogle Scholar
  47. 47.
    Soto, V., Garcia-Moratilla, S., Martinez-Munoz, G., Hernández-Lobato, D., Suarez, A.: A double pruning scheme for boosting ensembles. Cybern. IEEE Trans. 44(12), 2682–2695 (2014). DecCrossRefGoogle Scholar
  48. 48.
    Tang, EKe, Suganthan, P.N., Yao, X.: An analysis of diversity measures. Mach. Learn. 65(1), 247–271 (2006)CrossRefGoogle Scholar
  49. 49.
    Tsoumakas, G., Partalas, I., Vlahavas, I.: An ensemble pruning primer. In: Applications of supervised and unsupervised ensemble methods, pp. 1–13. Springer (2009)Google Scholar
  50. 50.
    Williams, G.: Use R: Data Mining with Rattle and R: the Art of Excavating Data for Knowledge Discovery. Springer, New York (2011)zbMATHGoogle Scholar
  51. 51.
    Wolpert, D.H.: Stacked generalization. Neural Netw. 5(2), 241–259 (1992)CrossRefGoogle Scholar
  52. 52.
    Yan, W., Goebel, K.F.: Designing classifier ensembles with constrained performance requirements. In: Defense and Security, International Society for Optics and Photonics, pp. 59–68 (2004)Google Scholar
  53. 53.
    Zhang, T., Ramakrishnan, R., Livny, M.: Birch: an efficient data clustering method for very large databases. In: ACM SIGMOD Record, vol. 25, pp. 103–114. ACM (1996)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Khaled Fawagreh
    • 1
  • Mohamed Medhat Gaber
    • 1
  • Eyad Elyan
    • 1
  1. 1.School of Computing Science and Digital MedialRobert Gordon UniversityAberdeenUK

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