A Modified Back-Propagation Algorithm to Deal with Severe Two-Class Imbalance Problems on Neural Networks
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.
Keywordstwo-class imbalance problems modified back-propagation re-sampling methods and SMOTE
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