Finding rare classes: adapting generative and discriminative models in active learning

Authors:
Timothy M. Hospedales;Shaogang Gong;Tao Xiang
Affiliations:
Queen Mary University of London;Queen Mary University of London;Queen Mary University of London
Venue:
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
Year:
2011

Citing 12
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Abstract

Discovering rare categories and classifying new instances of them is an important data mining issue in many fields, but fully supervised learning of a rare class classifier is prohibitively costly. There has therefore been increasing interest both in active discovery: to identify new classes quickly, and active learning: to train classifiers with minimal supervision. Very few studies have attempted to jointly solve these two inter-related tasks which occur together in practice. Optimizing both rare class discovery and classification simultaneously with active learning 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; and a classifier combination algorithm that switches generative and discriminative classifiers as learning progresses. Extensive evaluation on several standard datasets demonstrates the superiority of our approach over existing methods.