1. Ali, K.M., Pazzani, M.J.: Reducing the small disjuncts problem by learning probabilistic concept Descriptions. In: Petsche, T. (ed.) Computational Learning Theory and Natural Learning Systems, Volume 3, MIT Press, Cambridge, MA (1992)
2. Asuncion, A., Newman, D.J.: UCI Machine Learning Repository. University of California, Irvine, School of Information and Computer Science.
http://www.ics.uci.edu/mlearn/MLRepository.html
. Cited Sept 2008
3. Carvalho D.R., Freitas A.A.: A hybrid decision tree/genetic algorithm for coping with the problem of small disjuncts in data mining. In: Proceedings of the 2000 Genetic and Evolutionary Computation Conference, pp. 1061–1068 (2000)
4. Chawla N.V., Bowyer K.W., Hall L.O., Kegelmeyer W.P.: SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321–357 (2002)
5. Chawla N.V., Cieslak D.A., Hall L.O., Joshi A.: Automatically countering imbalance and its empirical relationship to cost. Data Mining and Knowledge Discovery, 17(2), 225–252 (2008)