One-sided Dynamic Undersampling No-Propagation Neural Networks for imbalance problem

Imbalanced problem occurs when the size of one class, i.e. the minority class, is much lower than that of the other classes, i.e. the majority classes. Conventional data level methods are employed as the preprocessing approaches to balance the datasets before the classifier learning. Since the balanced data remains unchanged during the learning process, one pre-deleted sample would never be used to train the classifier, which may result in information loss. To solve this problem, this work presents an One-sided Dynamic Undersampling (ODU) technique which adopts all samples in the training process, and dynamically determines whether a majority sample should be used for the classifier learning. Thus, ODU can dynamically undersample the majority class to balance the dataset. To validate the effectiveness of ODU, we integrate it into No-Propagation neural networks to propose an ODU No-Propagation Neural Networks (ODUNPNN). ODUNPNN takes all training samples into consideration, and dynamically undersamples majority class after each iteration, i.e. ODUNPNN integrates undersampling approach into the classifier learning process. Experimental results on both synthetic and real-world imbalance datasets demonstrate that ODUNPNN outperforms the NPNN-based algorithms, and results in comparative performance compared with LASVM-AL, EasyEnsemble, and DyS on real-world imbalance datasets. The contributions of this paper are: (1) ODUNPNN integrates undersampling approach into the classifier learning process. (2) ODUNPNN dynamically balances training data in each iteration. (3) ODU technique can be integrated into other classification learning machines.