Abstract
Recently, a new class of data-intensive application becomes widely recognized where data is modeled best as transient open-end streams rather than persistent tables on disk. It leads to a new surge of research interest called data streams. However, most of the reported works are concentrated on structural data, such as bit-sequences, and seldom focus on unstructural data, such as textual documents. In this paper, we propose an efficient classification approach for classifying high-speed text streams. The proposed approach is based on sketches such that it is able to classify the streams efficiently by scanning them only once, meanwhile consuming a small bounded of memory in both model maintenance and operation. Extensive experiments using benchmarks and a real-life news article collection are conducted. The encouraging results indicated that our proposed approach is highly feasible.
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References
Chakrabarti, S., Roy, S., Soundalgekar, M.V.: Fast and accurate text classification via multiple linear discriminant projections. In: Proceedings of the 28th Very Large Database Conference (2002)
Cristianini, N., Shaws-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-Based learning Methods. Cambridge University Press, Cambridge (2000)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley Interscience, Hoboken (2001)
Frakes, W.B., Baeza-Yates, R.: Information Retrieval: Data Structures and Algorithms. Prentice Hall PTR, Englewood Cliffs (1992)
Fung, G.P.C., Yu, J.X., Lam, W.: Automatic stock trend prediction by real time news. In: Proceedings of 2002 Workshop in Data Mining and Modeling (2002)
Greiff, W.R.: A theory of term weighting based on exploratory data analysis. In: Proceedings of SIGIR 1998 21th ACM International Conference on Research and Development in Information Retrieval, pp. 11–19 (1998)
Holt, J.D., Chung, S.M.: Efficient mining of association rules in text databases. In: Proceedings of 8th International Conference on Information and Knowledge Management, pp. 234–242 (1999)
Joachims, T.: Text categorization with support vector machines: Learning with many relevant features. In: Proceedings of 13th European Conference on Machine Learning, pp. 137–142 (1998)
Lewis, D.D.: An evaluation of phrasal and clustered representations on a text categorization task. In: Proceedings of SIGIR 1992 15th ACM International Conference on Research and Development in Information Retrieval, pp. 37–50 (1992)
Lewis, D.D.: Naive (bayes) at forty: The independence assumption in information retrieval. In: Proceedings of 13th European Conference on Machine Learning, pp. 4–15 (1998)
McCallum, A., Nigam, K.: A comparison of event models for naive bayes text classification. In: AAAI 1998 Workshop on Learning for Text Categorization (1998)
Meretakis, D., Fragoudis, D., Lu, H., Likothanassis, S.: Scalable association-based text classification. In: Proceedings of 10th International Conference on Information and Knowledge Management, pp. 5–11 (2001)
Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Information Processing and Management 24(5), 513–523 (1988)
Sebastiani, F.: Machine learning in automated text categorization. ACM Computing Surveys 34(1), 1–47 (2002)
Syed, N.A., Liu, H., Sung, K.K.: Incremental learning with support vector machines. In: Proceedings of SIGKDD 1999, 5th International Conference on Knowledge Discovery and Data Mining, pp. 313–321 (1999)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, Heidelberg (1995)
Yamamoto, K., Masuyama, S., Naito, S.: Automatic text classification method with simple class-weighting approach. In: Natural Language Processing Pacific Rim Symposium (1995)
Yang, Y.: An evaluation of statistical approaches to text categorization. Information Retrieval 2(1), 69–90 (1999)
Yang, Y., Liu, X.: A re-examination of text categorization methods. In: Proceedings of SIGIR 1999 22th ACM International Conference on Research and Development in Information Retrieval, pp. 42–49 (1999)
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Fung, G.P.C., Yu, J.X., Lu, H. (2003). Classifying High-Speed Text Streams. In: Dong, G., Tang, C., Wang, W. (eds) Advances in Web-Age Information Management. WAIM 2003. Lecture Notes in Computer Science, vol 2762. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45160-0_15
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DOI: https://doi.org/10.1007/978-3-540-45160-0_15
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