Abstract
The target of ensemble pruning is to increase efficiency by reducing the ensemble size of a multi classifier system and thus computational and storage costs, without sacrificing and preferably enhancing the generalization performance. However, most state-of-the-art ensemble pruning methods are based on unweighted or weighted voting ensembles; and their extensions to the Error Correcting Output Coding (ECOC) framework is not strongly evident or successful. In this study, a novel strategy for pruning ECOC ensembles which is based on a novel accuracy measure is presented. The measure is defined by establishing the link between the accuracies of the two-class base classifiers in the context of the main multiclass problem. The results show that the method outperforms the ECOC extensions of the state-of-the-art pruning methods in the majority of cases and that it is even possible to improve the generalization performance by only using 30% of the initial ensemble size in certain scenarios.
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References
Allwein, E.L., Schapire, R.E., Singer, Y.: Reducing multiclass to binary: A unifying approach for margin classifiers. JMLR 1, 113–141 (2000)
Asuncion, A., Newman, D.: UCI machine learning repository (2007), http://www.ics.uci.edu/~mlearn/MLRepository.html
Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)
Chandra, A., Yao, X.: Ensemble learning using multi-objective evolutionary algorithms. J. Math. Model. Algorithms 5(4), 417–445 (2006)
Demsar, J.: Statistical comparisons of classifiers over multiple data sets. JMLR 7, 1–30 (2006)
Dietterich, T.G., Bakiri, G.: Solving multiclass learning problems via error-correcting output codes. J. Artif. Intell. Res. (JAIR) 2, 263–286 (1995)
Hernandez-Lobato, D., Martinez-Munoz, G., Suarez, A.: Statistical instance-based pruning in ensembles of independent classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 364–369 (2009)
James, G., Hastie, T.: The error coding method and picts (1998)
Margineantu, D.D., Dietterich, T.G.: Pruning adaptive boosting. In: International Conference on Machine Learning, pp. 211–218 (1997)
Martinez-Munoz, G., Hernandez-Lobato, D., Suarez, A.: An analysis of ensemble pruning techniques based on ordered aggregation. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 245–259 (2009)
Martinez-Munoz, G., Suarez, A.: Aggregation ordering in bagging. In: Proc. of the IASTED ICAIA, pp. 258–263. Acta Press (2004)
Martinez-Munoz, G., Suarez, A.: Using boosting to prune bagging ensembles. Pattern Recognition Letters 28, 156–165 (2007)
Soto, V., Martínez-Muñoz, G., Hernández-Lobato, D., Suárez, A.: A double pruning algorithm for classification ensembles. In: El Gayar, N., Kittler, J., Roli, F. (eds.) MCS 2010. LNCS, vol. 5997, pp. 104–113. Springer, Heidelberg (2010)
Tsoumakas, G., Partalas, I., Vlahavas, I.: A taxonomy and short review of ensemble selection. In: ECAI 2008, Workshop SUEMA (2008)
Ulas, A., Yildiz, O.T., Alpaydin, E.: Cost-conscious comparison of supervised learning algorithms over multiple data sets. Pattern Recognition (2011)
Windeatt, T., Ghaderi, R.: Coding and decoding strategies for multi-class learning problems. Information Fusion 4(1), 11–21 (2003)
Zhang, Y., Burer, S., Street, W.N.: Ensemble pruning via semi-definite programming. JMLR 7, 1315–1338 (2006)
Zhou, Z.H.: Ensemble Methods: Foundations and Algorithms, 1st edn. CRC Press, Boca Raton (2012)
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Zor, C., Windeatt, T., Kittler, J. (2013). ECOC Matrix Pruning Using Accuracy Information. In: Zhou, ZH., Roli, F., Kittler, J. (eds) Multiple Classifier Systems. MCS 2013. Lecture Notes in Computer Science, vol 7872. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38067-9_34
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DOI: https://doi.org/10.1007/978-3-642-38067-9_34
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