Active Learning for Imbalanced Data: The Difficulty and Proportions of Class Matter

Abstract

Deep learning methods have achieved great success in many fields, but it is severely limited by the quantity and quality of training data. When the number of labeled samples is small and the datasets is class-imbalanced, the model is difficult to perform well. In this paper, an active learning method for the small and class-imbalanced labeled datasets is proposed. In addition to the uncertainty of instances which traditional active learning methods take into account, the difficulty and the proportion of samples of different classes are also considered in our method too, so that our active learning method is better oriented to the dataset with unbalanced classes. This paper first explains the motivation of the proposed method and then introduces the frameworks of the method in detail. Finally, experiments on three datasets prove that our method can obtain better results than traditional active learning methods based on uncertainty.