Cost Sensitive Learning of Deep Feature Representations from Imbalanced Data
This work addresses the challenge of learning from imbalanced data for real-world object detection and classification tasks, offering a method that avoids altering data distribution and reduces computational costs, though it is incremental in nature.
The authors tackled the problem of class imbalance in object detection and classification by proposing a cost-sensitive deep neural network that jointly optimizes class-dependent costs and network parameters, resulting in significantly outperforming baseline algorithms on six major image classification datasets.
Class imbalance is a common problem in the case of real-world object detection and classification tasks. Data of some classes is abundant making them an over-represented majority, and data of other classes is scarce, making them an under-represented minority. This imbalance makes it challenging for a classifier to appropriately learn the discriminating boundaries of the majority and minority classes. In this work, we propose a cost sensitive deep neural network which can automatically learn robust feature representations for both the majority and minority classes. During training, our learning procedure jointly optimizes the class dependent costs and the neural network parameters. The proposed approach is applicable to both binary and multi-class problems without any modification. Moreover, as opposed to data level approaches, we do not alter the original data distribution which results in a lower computational cost during the training process. We report the results of our experiments on six major image classification datasets and show that the proposed approach significantly outperforms the baseline algorithms. Comparisons with popular data sampling techniques and cost sensitive classifiers demonstrate the superior performance of our proposed method.