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Finding Rare Classes: Active Learning with Generative and Discriminative Models
Feb. 2013 (vol. 25 no. 2)
pp. 374-386
Timothy M. Hospedales, Queen Mary University of London, London
Shaogang Gong, Queen Mary University of London, London
Tao Xiang, Queen Mary University of London, London
Discovering rare categories and classifying new instances of them are important data mining issues in many fields, but fully supervised learning of a rare class classifier is prohibitively costly in labeling effort. There has therefore been increasing interest both in active discovery: to identify new classes quickly, and active learning: to train classifiers with minimal supervision. These goals occur together in practice and are intrinsically related because examples of each class are required to train a classifier. Nevertheless, very few studies have tried to optimise them together, meaning that data mining for rare classes in new domains makes inefficient use of human supervision. Developing active learning algorithms to optimise both rare class discovery and classification simultaneously is challenging because discovery and classification have conflicting requirements in query criteria. In this paper, we address these issues with two contributions: a unified active learning model to jointly discover new categories and learn to classify them by adapting query criteria online; and a classifier combination algorithm that switches generative and discriminative classifiers as learning progresses. Extensive evaluation on a batch of standard UCI and vision data sets demonstrates the superiority of this approach over existing methods.
Index Terms:
Uncertainty,Data models,Adaptation models,Training,Support vector machines,Entropy,Approximation methods,discriminative models,Active learning,rare class discovery,imbalanced learning,classification,generative models
Citation:
Timothy M. Hospedales, Shaogang Gong, Tao Xiang, "Finding Rare Classes: Active Learning with Generative and Discriminative Models," IEEE Transactions on Knowledge and Data Engineering, vol. 25, no. 2, pp. 374-386, Feb. 2013, doi:10.1109/TKDE.2011.231
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