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

Timothy M. Hospedales, Shaogang Gong, Tao Xiang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

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.
Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining
Subtitle of host publication15th Pacific-Asia Conference, PAKDD 2011, Shenzhen, China, May 24-27, 2011, Proceedings, Part II
PublisherSpringer Berlin Heidelberg
Number of pages13
ISBN (Electronic)978-3-642-20847-8
ISBN (Print)978-3-642-20846-1
Publication statusPublished - 2011

Publication series

NameLecture Notes in Computer Science (LNCS)
PublisherSpringer Berlin Heidelberg
ISSN (Print)0302-9743


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