Rare category detection is an open challenge for active learning, especially in the de-novo case (no labeled examples), but of significant practical importance for data mining - e.g. detecting new financial transaction fraud patterns, where normal legitimate transactions dominate. This paper develops a new method for detecting an instance of each minority class via an unsupervised local-density-differential sampling strategy. Essentially a variable-scale nearest neighbor process is used to optimize the probability of sampling tightly-grouped minority classes, subject to a local smoothness assumption of the majority class. Results on both synthetic and real data sets are very positive, detecting each minority class with only a fraction of the actively sampled points required by random sampling and by Pelleg's Interleave method, the prior best technique in the sparse literature on this topic.
['Jingrui He', 'Jaime G. Carbonell']
Nearest-Neighbor-Based Active Learning for Rare Category Detection