92
Boriah, S., Chandola, V., Kumar, V., 2007, Similarity Measures for Categorical Data: A
Comparative Evaluation, In: Proceedings of the SIAM International Conference on Data
Mining, Minneapolis, Minnesota, USA, pp. 243-254.
Beckmann, M., de Lima, B. S. L. P., Ebecken, N.F.F., 2009, Algoritmos Genéticos
como Estratégia de Pré-Processamento para o Aprendizado de Máquina em Conjuntos
de Dados Desbalanceados, CILAMCE/2009 – Congresso Latino Americano em
Métodos Computacionais em Engenharia, Búzios, Brasil.
Bifet, A., Holmes, G., Kirkby, R., Pfahringer, B., 2010, MOA: Massive Online
Analysis, Journal of Machine Learning Research, vol. 11, pp. 1601-1604.
Blake, C., & Merz, C., 1998, UCI Repository of Machine Learning Databases,
disponível em: http://www.ics.uci.edu/~mlearn/~MLRepository.html, Department of
Information and Computer Sciences, University of California, Irvine.
Booch, G., Jacobson, I., and Rumbaugh, J., 1999, Unified Modeling Language – User’s
Guide, 2 ed., Addison–Wesley.
Boley, D.L., 1998, Principal Direction Divisive Partitioning, Data Mining and
Knowledge Discovery, vol. 2, no. 4, pp. 325-344.
Breiman, L., Friedman, J., Olshen, R., Stone C., 1984, Classification and Regression
Trees, Monterey, CA, USA, Wadsworth and Brooks.
Breiman, L., 1996, Heuristics of Instability and Stabilization in Model Selection, The
Annals of Statistics, vol. 24, no. 6, 2350-2383.
Carvalho, D.R., Freitas, A.A., 2002, A genetic algorithm for discovering small-disjunct
rules in data mining. Applied Soft Computing vol. 2, issue 2, pp. 75-88.
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P., 2002, SMOTE:
SyntheticMinority Over-sampling Technique, JAIR vol. 16, pp. 321-357.