document: Large-scale text categorization by batch mode active learning 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 large-scale text categorization for World Wide Web documents .
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