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Class imbalance is a long-standing problem relevant to a number of real-world applications of deep learning. Oversampling techniques, which are effective for handling class imbalance in classical learning systems, can not be directly applied to end-to-end deep learning systems. We propose a three-player adversarial game between a convex generator, a multi-class classifier network, and a real/fake discriminator to perform oversampling in deep learning systems. The convex generator generates new samples from the minority classes as convex combinations of existing instances, aiming to fool both the discriminator as well as the classifier into misclassifying the generated samples. Consequently, the artificial samples are generated at critical locations near the peripheries of the classes. This, in turn, adjusts the classifier induced boundaries in a way which is more likely to reduce misclassification from the minority classes. Extensive experiments on multiple class imbalanced image datasets establish the efficacy of our proposal.

The classification accuracy of a -nearest neighbor ( NN) classifier is largely dependent on the choice of the number of nearest neighbors denoted by . However, given a data set, it is a tedious task to optimize the performance of NN by tuning . Moreover, the performance of NN degrades in the presence of class imbalance, a situation characterized by disparate representation from different classes. We aim to address both the issues in this paper and propose a variant of NN called the Adaptive NN (Ada- NN). The Ada- NN classifier uses the density and distribution of the neighborhood of a test point and learns a suitable point-specific for it with the help of artificial neural networks. We further improve our proposal by replacing the neural network with a heuristic learning method guided by an indicator of the local density of a test point and using information about its neighboring training points. The proposed heuristic learning algorithm preserves the simplicity of NN without incurring serious computational burden. We call this method Ada- NN2. Ada- NN and Ada- NN2 perform very competitive when compared with NN, five of NN's state-of-the-art variants, and other popular classifiers. Furthermore, we propose a class-based global weighting scheme (Global Imbalance Handling Scheme or GIHS) to compensate for the effect of class imbalance. We perform extensive experiments on a wide variety of data sets to establish the improvement shown by Ada- NN and Ada- NN2 using the proposed GIHS, when compared with NN, and its 12 variants specifically tailored for imbalanced classification.

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