Skip to main content
SpringerLink
Log in
Book cover

International Symposium on Methodologies for Intelligent Systems

ISMIS 2003: Foundations of Intelligent Systems pp 315–319Cite as

Improving the Performance of Text Classifiers by Using Association Features

  • Conference paper

  • 483 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2871))

Abstract

The co-occurrence of words can make contribution to text classification. However, current text classification technology failed to take full advantage of this information. In this paper, we use association features to describe this information and present the algorithm for creating association feature set to make the association features we selected be good discriminators. The experiment results show that the performance of Naïve Bayes text classifier and decision tree text classifier could be improved by using association features.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Deshpande, M., Karypis, G.: Using Conjunction of Attribute Values for Classification. In: Proc. of 11th ACM Int. Conf. Information and Knowledge Management, CIKM 2002 (2002)

    Google Scholar 

  2. Lesh, N., Zaki, M.J., Ogihara, M.: Mining features for Sequence Classification. In: Proc. of 5th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD 1999) (1999)

    Google Scholar 

  3. Mladenic, D., Grobelnik, M.: Word Sequences as Features in Text-learning. In: Proc. of the 17th Electrotechnical and Computer Science Conference, Ljubljana, Slovenia (1998)

    Google Scholar 

  4. Tan, C.-M., Wang, Y.-F., Lee, C.-D.: The Use of Bigrams to Enhance Text Categorization. Information Processing and Management 1(38) (2002)

    Google Scholar 

  5. Sebastiani, F.: Machine learning in automated text categorization. ACM Computing Surveys 34(1), 1–47 (2002)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dept. Computer Science & Engineering, Northwestern Polytechnical University, China

    Yang Zhang, Lijun Zhang, Zhanhuai Li & Jianfeng Yan

Authors
  1. Yang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lijun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhanhuai Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jianfeng Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. The International WIC Institute, Beijing University of Technology, China

    Ning Zhong

  2. Department of Computer Science, University of North Carolina, NC 28223, Charlotte, USA

    Zbigniew W. Raś

  3. Shimane University, 89-1 Enya-cho Izumo, 6938501, Shimane, Japan

    Shusaku Tsumoto

  4. Department of Informatics, Graduate School of Information Science and Electrical Engineering, Kyushu University, 744 Motooka, Nishi, 819-0395, Fukuoka, Japan

    Einoshin Suzuki

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Zhang, Y., Zhang, L., Li, Z., Yan, J. (2003). Improving the Performance of Text Classifiers by Using Association Features. In: Zhong, N., Raś, Z.W., Tsumoto, S., Suzuki, E. (eds) Foundations of Intelligent Systems. ISMIS 2003. Lecture Notes in Computer Science(), vol 2871. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39592-8_43

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-39592-8_43

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-20256-1

  • Online ISBN: 978-3-540-39592-8

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation