Abstract
Random Forest is an ensemble learning method used for classification and regression. In such an ensemble, multiple classifiers are used where each classifier casts one vote for its predicted class label. Majority voting is then used to determine the class label for unlabelled instances. Since it has been proven empirically that ensembles tend to yield better results when there is a significant diversity among the constituent models, many extensions were developed during the past decade that aim at inducing some diversity in the constituent models in order to improve the performance of Random Forests in terms of both speed and accuracy. In this paper, we propose a method to promote Random Forest diversity by using randomly selected subspaces, giving a weight to each subspace according to its predictive power, and using this weight in majority voting. Experimental study on 15 real datasets showed favourable results, demonstrating the potential of the proposed method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Adeva, J.J.G., Beresi, U., Calvo, R.: Accuracy and diversity in ensembles of text categorisers. CLEI Electronic Journal 9(1) (2005)
Amit, Y., Geman, D.: Shape quantization and recognition with randomized trees. Neural Computation 9(7), 1545–1588 (1997)
Bache, K., Lichman, M.: UCI machine learning repository (2013)
Bader-El-Den, M., Gaber, M.: Garf: towards self-optimised random forests. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds.) ICONIP 2012, Part II. LNCS, vol. 7664, pp. 506–515. Springer, Heidelberg (2012)
Bernard, S., Heutte, L., Adam, S.: A study of strength and correlation in random forests. In: Huang, D.-S., McGinnity, M., Heutte, L., Zhang, X.-P. (eds.) ICIC 2010. CCIS, vol. 93, pp. 186–191. Springer, Heidelberg (2010)
Boinee, P., De Angelis, A., Foresti, G.L.: Meta random forests. International Journal of Computationnal Intelligence 2(3), 138–147 (2005)
Breiman, L.: Bagging predictors. Machine Learning 24(2), 123–140 (1996)
Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)
Brown, G., Wyatt, J., Harris, R., Yao, X.: Diversity creation methods: a survey and categorisation. Information Fusion 6(1), 5–20 (2005)
Cai, Q.-T., Peng, C.-Y., Zhang, C.-S.: A weighted subspace approach for improving bagging performance. In: IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2008, pp. 3341–3344. IEEE (2008)
Cuzzocrea, A., Francis, S.L., Gaber, M.M.: An information-theoretic approach for setting the optimal number of decision trees in random forests. In: 2013 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 1013–1019. IEEE (2013)
Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55(1), 119–139 (1997)
García-Pedrajas, N., Ortiz-Boyer, D.: Boosting random subspace method. Neural Networks 21(9), 1344–1362 (2008)
Ho, T.K.: Random decision forests. In: Proceedings of the Third International Conference on Document Analysis and Recognition, vol. 1, pp. 278–282. IEEE (1995)
Ho, T.K.: The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(8), 832–844 (1998)
Kuncheva, L.I., Whitaker, C.J.: Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Machine Learning 51(2), 181–207 (2003)
Liaw, A., Wiener, M.: Classification and regression by randomforest. R News 2(3), 18–22 (2002)
Maclin, R., Opitz, D.: Popular ensemble methods: An empirical study. arXiv preprint arXiv:1106.0257 (2011)
Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and regression trees. Wadsworth International Group (1984)
Opitz, D.W.: Feature selection for ensembles. In: AAAI/IAAI, pp. 379–384 (1999)
Panov, P., Džeroski, S.: Combining bagging and random subspaces to create better ensembles. Springer (2007)
Polikar, R.: Ensemble based systems in decision making. IEEE Circuits and Systems Magazine 6(3), 21–45 (2006)
Robnik-Šikonja, M.: Improving random forests. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) ECML 2004. LNCS (LNAI), vol. 3201, pp. 359–370. Springer, Heidelberg (2004)
Rokach, L.: Ensemble-based classifiers. Artificial Intelligence Review 33(1-2), 1–39 (2010)
Tang, K., Suganthan, P.N., Yao, X.: An analysis of diversity measures. Machine Learning 65(1), 247–271 (2006)
Wolpert, D.H.: Stacked generalization. Neural Networks 5(2), 241–259 (1992)
Yan, W., Goebel, K.F.: Designing classifier ensembles with constrained performance requirements. In: Defense and Security, pp. 59–68. International Society for Optics and Photonics (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Fawagreh, K., Gaber, M.M., Elyan, E. (2014). Diversified Random Forests Using Random Subspaces. In: Corchado, E., Lozano, J.A., Quintián, H., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2014. IDEAL 2014. Lecture Notes in Computer Science, vol 8669. Springer, Cham. https://doi.org/10.1007/978-3-319-10840-7_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-10840-7_11
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10839-1
Online ISBN: 978-3-319-10840-7
eBook Packages: Computer ScienceComputer Science (R0)