Active learning for hierarchical text classification

Authors:
Xiao Li;Da Kuang;Charles X. Ling
Affiliations:
Department of Computer Science, The University of Western Ontario, London, Ontario, Canada;Department of Computer Science, The University of Western Ontario, London, Ontario, Canada;Department of Computer Science, The University of Western Ontario, London, Ontario, Canada
Venue:
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Year:
2012

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Abstract

Hierarchical text classification plays an important role in many real-world applications, such as webpage topic classification, product categorization and user feedback classification. Usually a large number of training examples are needed to build an accurate hierarchical classification system. Active learning has been shown to reduce the training examples significantly, but it has not been applied to hierarchical text classification due to several technical challenges. In this paper, we study active learning for hierarchical text classification. We propose a realistic multi-oracle setting as well as a novel active learning framework, and devise several novel leveraging strategies under this new framework. Hierarchical relation between different categories has been explored and leveraged to improve active learning further. Experiments show that our methods are quite effective in reducing the number of oracle queries (by 74% to 90%) in building accurate hierarchical classification systems. As far as we know, this is the first work that studies active learning in hierarchical text classification with promising results.