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Large-Scale Text Categorization by Batch Mode Active Learning

  • Steven C. H. Hoi, Department of Computer Science and Engineering, The Chinese University of Hong Kong, China
  • Rong Jin, Department of Computer Science and Engineering, Michigan State University, USA
  • Michael R. Lyu, Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong

Full text:

Track: Data Mining

Large-scale text categorization is an important research topic for Web data mining. One of the challenges in large-scale text categorization is how to reduce the human efforts in labeling text documents for building reliable classification models. In the past, there have been many studies on applying active learning methods to automatic text categorization, which try to select the most informative documents for labeling manually. Most of these studies focused on selecting a single unlabeled document in each iteration. As a result, the text categorization model has to be retrained after each labeled document is solicited. In this paper, we present a novel active learning algorithm that selects a batch of text documents for labeling manually in each iteration. The key of the batch mode active learning is how to reduce the redundancy among the selected examples such that each example provides unique information for model updating. To this end, we use the Fisher information matrix as the measurement of model uncertainty and choose the set of documents to effectively maximize the Fisher information of a classification model. Extensive experiments with three different datasets have shown that our algorithm is more effective than the state-of-the-art active learning techniques for text categorization and can be a promising tool toward largescale text categorization for World Wide Web documents.

Citation

Hoi, S. C., Jin, R., and Lyu, M. R. 2006. Large-scale text categorization by batch mode active learning. In Proceedings of the 15th International Conference on World Wide Web (Edinburgh, Scotland, May 23 - 26, 2006). WWW '06. ACM Press, New York, NY, 633-642.
DOI= http://doi.acm.org/10.1145/1135777.1135870

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