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PAKDD
2011
ACM

Finding Rare Classes: Adapting Generative and Discriminative Models in Active Learning

8 years 3 months ago
 Finding Rare Classes: Adapting Generative and Discriminative Models in Active Learning
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 st...
Timothy Hospedales, Shaogang Gong and Tao Xiang
Added 22 Jun 2011
Updated 22 Jun 2011
Type Conference
Year 2011
Where PAKDD
Authors Timothy Hospedales, Shaogang Gong and Tao Xiang
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