Abstract
Traditional text classification techniques are based on a basic assumption that the underlying distributions of training and test data should be identical. However, in many real world applications, this assumption is not often satisfied. Labeled training data are expensive, but there may be some labeled data available in a different but related domain from test data. Therefore, how to make use of labeled data from a different domain to supervise the classification becomes a crucial task. In this paper, we propose a novel algorithm for cross-domain text classification using reinforcement learning. In our algorithm, the training process is iteratively reinforced by making use of the relations between documents and words. Empirically, our method is an effective and scalable approach for text categorization when the training and test data are from different but related domains. The experimental results show that our algorithm can achieve better performance than several state-of-art classifiers.
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References
Mitchell, T.M.: 6. In: Machine Learning, p. 179. McGraw Hill, New York (1997)
Schmidhuber, J.: On learning how to learn learning strategies. Technical Report FKI-198-94, Fakultat fur Informatik (1994)
Caruana, R.: Multitask learning. Machine Learning 28(1), 41–75 (1997)
Lewis, D.D.: Representation and learning in information retrieval. Ph.D thesis, Amherst, MA, USA (1992)
Boser, B.E., Guyon, I., Vapnik, V.: A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory (1992)
Zhu, X.: Semi-supervised learning literature survey. Technical Report 1530, University of Wisconsin–Madison (2006)
Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Proceedings of the Eleventh Annual Conference on Computational Learning Theory (1998)
Nigam, K., McCallum, A.K., Thrun, S., Mitchell, T.: Text classification from labeled and unlabeled documents using em. Machine Learning 39, 103–134 (2000)
Joachims, T.: Transductive inference for text classification using support vector machines. In: Proceedings of Sixteenth International Conference on Machine Learning (1999)
Ben-David, S., Schuller, R.: Exploiting task relatedness for multiple task learning. In: Proceedings of the Sixteenth Annual Conference on Learning Theory (2003)
Bennett, P.N., Dumais, S.T., Horvitz, E.: Inductive transfer for text classification using generalized reliability indicators. In: Proceedings of ICML 2003 Workshop on The Continuum from Labeled and Unlabeled Data (2003)
Swarup, S., Ray, S.R.: Cross-domain knowledge transfer using structured representations. In: Proceedings of the Twenty-First National Conference on Artificial Intelligence (2006)
DauméIII, H., Marcu, D.: Domain adaptation for statistical classifiers. Journal of Artificial Intelligence Research 26, 101–126 (2006)
Raina, R., Ng, A.Y., Koller, D.: Constructing informative priors using transfer learning. In: Proceedings of Twenty-Third International Conference on Machine Learning (2006)
Wang, J., Zeng, H., Chen, Z., Lu, H., Tao, L., Ma, W.: ReCoM: reinforcement clustering of multi-type interrelated data objects. In: Proceedings of the 26th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 274–281 (2003)
Xue, G., Shen, D., Yang, Q., Zeng, H., Chen, Z., Yu, Y., Xi, W., Ma, W.: IRC: An Iterative Reinforcement Categorization Algorithm for Interrelated Web Objects. In: ICDM 2004: Proceedings of the 4th IEEE International Conference on Data Mining, pp. 273–280 (2004)
Lang, K.: Newsweeder: Learning to filter netnews. In: Proceedings of the Twelfth International Conference on Machine Learning (1995)
McCallum, A.K.: Simulated/real/aviation/auto usenet data, http://www.cs.umass.edu/~mccallum/code-data.html
Lewis, D.D.: Reuters-21578 test collection, http://www.daviddlewis.com/
Porter, M.F.: An algorithm for suffix stripping. Program 14(3), 130–137 (1980)
Yang, Y., Pedersen, J.O.: A comparative study on feature selection in text categorization. In: Proceedings of Fourteenth International Conference on Machine Learning (1997)
Karypis, G.: Cluto – software for clustering high-dimensional datasets, http://glaros.dtc.umn.edu/gkhome/views/cluto
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Zhang, D., Xue, GR., Yu, Y. (2008). Iterative Reinforcement Cross-Domain Text Classification. In: Tang, C., Ling, C.X., Zhou, X., Cercone, N.J., Li, X. (eds) Advanced Data Mining and Applications. ADMA 2008. Lecture Notes in Computer Science(), vol 5139. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88192-6_27
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DOI: https://doi.org/10.1007/978-3-540-88192-6_27
Publisher Name: Springer, Berlin, Heidelberg
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