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 amount of 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 manually labeling. 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 manually labeling 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 matr...

• Type: Conference
• Year: 2006
• Where: WWW
• Authors: Steven C. H. Hoi, Rong Jin, Michael R. Lyu
• Keywords: Internet Technology, Keywords Text Categorization, Large-scale Text Categorization, Text Categorization Model, WWW 2006