Active one-class learning by kernel density estimation

Active learning has been a popular area of research in recent years. It can be used to improve the performance of learning tasks by asking the labels of unlabeled data from the user. In these methods, the goal is to achieve the highest possible accuracy gain while posing minimum queries to the user. The existing approaches for active learning have been mostly applicable to the traditional binary or multi-class classification problems. However, in many real-world situations, we encounter problems in which we have access only to samples of one class. These problems are known as one-class learning or outlier detection problems and the User relevance feedback in image retrieval systems is an example of such problems. In this paper, we propose an active learning method which uses only samples of one class. We use kernel density estimation as the baseline of one-class learning algorithm and then introduce some confidence criteria to select the best sample to be labeled by the user. The experimental results on real world and artificial datasets show that in the proposed method, the average gain in accuracy is increased significantly, compared to the popular random unlabeled sample selection strategy.