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East European Conference on Advances in Databases and Information Systems

ADBIS 2002: Advances in Databases and Information Systems pp 65–79Cite as

An Approach to Improve Text Classification Efficiency

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2435))

Abstract

Text classification is becoming more and more important with the rapid growth of on-line information available. In this paper, we propose an approach to speedup the process of text classification based on pruning the training corpus. Effective algorithm for text corpus pruning is designed. Experiments over real-world text corpus are carried out, which validates the effectiveness and efficiency of the proposed approach. Our approach is especially suitable for applications of on-line text classification.

This work was supported by the Provincial Natural Science Foundation of Hubei of China (No. 2001ABB050) and the Natural Science Foundation of China (NSFC) (No. 60173027).

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Author information

Authors and Affiliations

  1. State Key Lab of Software Engineering, Wuhan University, 430072, Wuhan, China

    Shuigeng Zhou

  2. School of Computer Science, Wuhan University, 430072, Wuhan, China

    Jihong Guan

Authors
  1. Shuigeng Zhou
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  2. Jihong Guan
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Editor information

Editors and Affiliations

  1. Department of Informatics, Aristotle University, 54006, Thessaloniki, Greece

    Yannis Manolopoulos

  2. Department of Computer Science and Engineering, Slovak University of Technology, Ilkovicova 3, 81219, Bratislava, Slovakia

    Pavol Návrat

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© 2002 Springer-Verlag Berlin Heidelberg

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Zhou, S., Guan, J. (2002). An Approach to Improve Text Classification Efficiency. In: Manolopoulos, Y., Návrat, P. (eds) Advances in Databases and Information Systems. ADBIS 2002. Lecture Notes in Computer Science, vol 2435. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45710-0_6

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  • DOI: https://doi.org/10.1007/3-540-45710-0_6

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44138-0

  • Online ISBN: 978-3-540-45710-7

  • eBook Packages: Springer Book Archive

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