An overlap sensitive neural network for class imbalanced data

Class imbalance is one of the well-known challenges in machine learning. Class imbalance occurs when one class dominates the other class in terms of the number of observations. Due to this imbalance, conventional classifiers fail to classify the minority class correctly. The challenges become even more severe when class overlap occurs in imbalanced data. Though literature is available to sequentially deal with class imbalance and class overlap, these methods are quite complex and not so efficient. In this paper, we propose an overlap-sensitive artificial neural network that can handle the problem of class overlapping and class imbalance simultaneously, along with noisy and outlier observations. The strength of this method lies in identifying the overlapping observations rather than the region and in not using multiple classifiers unlike the other existing methods. The key idea of the proposed method is in weighing the observations based on its location in the feature space before training the neural network. The performance of the proposed method is evaluated on 12 simulated data sets and 23 real-life data sets and compared with other well known methods.The results clearly indicate the strength and ability of the proposed method for a wide variety of imbalance ratio and levels of overlapping. Also, it is shown that the proposed method is statistically superior to the other methods in terms of different performance measures.