1. Barandela, R., Cortès, N., Palacios, A.: The nearest neighbor rule and the reduction of the training sample size. In: Proc. 9th Spanish Symp. on Pattern Recognition and Image Analysis, vol. 1, pp. 103–108 (2001)
2. Barandela, R., Sánchez, J.S., García, V., Rangel, E.: Strategies for learning in class imbalance problems. Pattern Recognition 36, 849–851 (2003)
3. Chan, P., Stolfo, S.: Toward scalable learning with non-uniform class and cost distributions: a case study in credit card fraud detection. In: Proc. 4th Int. Conf. on Knowledge Discovery and Data Mining, pp. 164–168 (1998)
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 (2000)
5. Chen, C.H., Józwik, A.: A sample set condensation algorithm for the class sensitive artificial neural network. Pattern Recognition Letters 17, 819–823 (1996)