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English Information

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Authors
# Name
1 Maria Luiza de Sousa(marialuiza.silva@usp.br)
2 Marcelo Lauretto(marcelolauretto@usp.br)
3 Helena Brentani(helena.brentani@gmail.com)
4 Fátima Nunes(fatima.nunes@usp.br)
5 Ariane Machado-Lima(ariane.machado@usp.br)

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Reference
# Reference
1 Charte, F., Rivera, A. J., del Jesus, M. J., and Herrera, F. (2015). MLSMOTE: Approaching imbalanced multilabel learning through synthetic instance generation. KnowledgeBased Systems, 89:385–397.
2 Madjarov, G., Kocev, D., Gjorgjevikj, D., and Dzeroski, S. (2012). An extensive experimental comparison of methods for multi-label learning. Pattern Recognition, 45(9):3084–3104.
3 Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in python. Journal of Machine Learning Research, 12:2825–2830.
4 Tomás, J. T., Spolaôr, N., Cherman, E. A., and Monard, M. C. (2014). A framework to generate synthetic multi-label datasets. Electronic Notes in Theoretical Computer Science, 302:155–176
5 Tsoumakas, G. and Katakis, I. (2007). Multi-label classification: An overview. In International Journal of Data Warehousing and Mining.
6 Zhang, M.-L. and Zhou, Z.-H. (2014). A review on multi-label learning algorithms. IEEE Transactions on Knowledge and Data Engineering, 26(8):1819–1837.