Skip to main content
SpringerLink
Log in

Text Categorization: Evaluation

  • Chapter
  • First Online:
Text Mining

Part of the book series: Studies in Big Data ((SBD,volume 45))

  • 4047 Accesses

Abstract

This chapter is concerned with the schemes of evaluating text categorization systems.

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

Access this chapter

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 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover 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

Institutional subscriptions

References

  1. Cai, D., He, X.: Manifold adaptive experimental design for text categorization. IEEE Trans. Knowl. Data Eng. 24, 707–719 (2011)

    Article  Google Scholar 

  2. Dong, Y., Han, K.: Boosting SVM classifiers by ensemble. In: Proceeding WWW ‘05 Special Interest Tracks and Posters of the 14th International Conference on World Wide Web, pp. 1072–1073 (2005)

    Google Scholar 

  3. Jo, T.: Neural based approach to keyword extraction from documents. Lect. Note Comput. Sci. 2667, 456–461 (2003)

    Article  Google Scholar 

  4. Jo, T.: Modified version of SVM for text categorization. Int. J. Fuzzy Log. Intell. Syst. 8, 52–60 (2008)

    Article  Google Scholar 

  5. Jo, T.: NTC (Neural Text Categorizer): neural network for text categorization. Int. J. Inf. Stud. 2, 83–96 (2010)

    Google Scholar 

  6. Jo, T.: Application of table based similarity to classification of bio-medical documents. In: The Proceedings of IEEE International Conference on Granular Computing, pp. 162–166 (2013)

    Google Scholar 

  7. Jo, T.: Simulation of numerical semantic operations on strings in medical domain. In: The Proceedings of IEEE International Conference on Granular Computing, pp. 167–171 (2013)

    Google Scholar 

  8. Jo, T.: Index optimization with KNN considering similarities among features. In: The Proceedings of 14th International Conference on Advances in Information and Knowledge Engineering, pp. 120–124 (2015)

    Google Scholar 

  9. Joachims, T.: Text categorization with support vector machines: learning with many relevant features. In: Proceedings of European Conference on Machine Learning, pp. 137–142 (1998)

    Chapter  Google Scholar 

  10. Kreuzthaler, M., Bloice, M.D., Faulstich, L., Simonic, K.M., Holzinger, A.: A comparison of different retrieval strategies working on medical free texts. J. Univers. Comput. Sci. 17, 1109–1133 (2011)

    Google Scholar 

  11. Li, T., Zhu, S., Ogihara, M.: Hierarchical document classification using automatically generated hierarchy. J. Intell. Inf. Syst. 29, 211–230 (2007)

    Article  Google Scholar 

  12. Sebastiani, F.: Machine learning in automated text categorization. ACM Comput. Surv. 34, 1–47 (2002)

    Article  Google Scholar 

  13. Tan, S.: An effective refinement strategy for KNN text classifier. Expert Syst. Appl. 30, 290–298 (2006)

    Article  Google Scholar 

  14. Yang, Y.: An evaluation of statistical approaches to MEDLINE indexing. In: Proceedings of the AMIA Annual Fall Symposium, pp. 358–362 (1996)

    Google Scholar 

  15. Yang, Y., Liu, X.: A re-examination of text categorization methods. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 42–49 (1999)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Game, Hongik University, Seoul, Korea (Republic of)

    Taeho Jo

Authors
  1. Taeho Jo
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Jo, T. (2019). Text Categorization: Evaluation. In: Text Mining. Studies in Big Data, vol 45. Springer, Cham. https://doi.org/10.1007/978-3-319-91815-0_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-91815-0_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-91814-3

  • Online ISBN: 978-3-319-91815-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation