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English Information

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Authors
# Name
1 Raphael Campos(rcampos@dcc.ufmg.br)
2 Marcos Gonçalves(mgoncalv@dcc.ufmg.br)

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Reference
# Reference
1 Breiman, L. (1996). Bagging predictors. Mach. Learn., 24(2):123–140.
2 Breiman, L. (2001). Random forests. Machine Learning, 45(1):5–32.
3 Fernández-Delgado, M., Cernadas, E., Barro, S., and Amorim, D. (2014). Do we need hundreds of classifiers to solve real world classification problems? J. Mach. Learn. Res., 15(1):3133–3181.
4 Freund, Y. and Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci., 55(1):119–139.
5 Geurts, P., Ernst, D., and Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1):3–42.
6 Hastie, T., Tibshirani, R., and Friedman, J. H. (2009). The Elements of Statistical Learning. Springer.
7 Salles, T., Gonçalves, M., Rodrigues, V., and Rocha, L. (2015). Broof: Exploiting out-of-bag errors, boosting and random forests for effective automated classification. In Proc. of the 38th International ACM SIGIR Conference on Inf. Retrieval, pages 353–362.
8 Segal, M. R. (2004). Machine learning benchmarks and random forest regression. Technical report, University of California.