Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
5-2006
Abstract
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.
Keywords
text categorization, active learning, logistic regression, Fisher information, convex optimization
Discipline
Computer Sciences | Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
WWW '06: Proceedings of the 15th International Conference on World Wide Web, Edinburgh, Scotland, May 23-26
First Page
633
Last Page
642
ISBN
9781595933232
Identifier
10.1145/1135777.1135870
Publisher
ACM
City or Country
New York
Citation
HOI, Steven C. H.; JIN, Rong; and LYU, Michael R..
Large-Scale Text Categorization by Batch Mode Active Learning. (2006). WWW '06: Proceedings of the 15th International Conference on World Wide Web, Edinburgh, Scotland, May 23-26. 633-642.
Available at: https://ink.library.smu.edu.sg/sis_research/2390
Copyright Owner and License
Publisher
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/1135777.1135870