Abstract
In this paper we propose a modified back-propagation to deal with severe two-class imbalance problems. The method consists in automatically to find the over-sampling rate to train a neural network (NN), i.e., identify the appropriate number of minority samples to train the NN during the learning stage, so to reduce training time. The experimental results show that the performance proposed method is a very competitive when it is compared with conventional SMOTE, and its training time is lesser.
This work has been partially supported under grants of: Projects 3072/2011 from the UAEM, PROMEP/103.5/11/3796 from the Mexican SEP and SDMAIA-010 of the TESJO.
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Alejo, R., Toribio, P., Valdovinos, R.M., Pacheco-Sanchez, J.H. (2012). A Modified Back-Propagation Algorithm to Deal with Severe Two-Class Imbalance Problems on Neural Networks. In: Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F., Olvera López, J.A., Boyer, K.L. (eds) Pattern Recognition. MCPR 2012. Lecture Notes in Computer Science, vol 7329. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31149-9_27
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DOI: https://doi.org/10.1007/978-3-642-31149-9_27
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