Nothing Special   »   [go: up one dir, main page]

Skip to main content

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4224))

Abstract

In this paper, the grouping method of the similar words, is proposed for the classification of documents. It is shown that the grouping of words has equivalent ability to the LSA in the classification accuracy. Further, a new combining method is proposed for the documents classification, which consists of Grouping, Latent Semantic Analysis(LSA) followed by the k-Nearest Neighbor classification ( k-NN ). The combining method proposed here, shows the higher accuracy in the classification than the conventional methods of the kNN, and the LSA followed by the kNN. Thus, the grouping method is effective as a preprocessing before the conventional method.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Grossma, D.A., Frieder, O.: Information Retrieval - Algorithms and Heuristics-, p. 332. Springer, Heidelberg (2004)

    Google Scholar 

  2. Sebastiani, F.: A tutorial on automated text categorization. In: Proc. of ASAI-1999, 1st Argentinian Symposium on Artificial Intelligence, Buenos Aires, pp. 7–35 (1999)

    Google Scholar 

  3. Derrwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by latent semantic analysis. Journal of the American Society for Information Science 41, 391–407 (1990)

    Article  Google Scholar 

  4. Landauer, P.W., Folz, T.K., Laham, D.: Introduction to latent semantic analysis. Discourse Processes 25, 259–284 (1998)

    Article  Google Scholar 

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

    Article  Google Scholar 

  6. Bao, Y., Ishii, N.: Combining multiple k-nearest neighbor classifiers for text classification by reducts. In: Lange, S., Satoh, K., Smith, C.H. (eds.) DS 2002. LNCS, vol. 2534, pp. 340–347. Springer, Heidelberg (2002)

    Google Scholar 

  7. Sirmakessis, S.: Text Mining and its Application, p. 204. Springer, Heidelberg (2003)

    Google Scholar 

  8. Baldi, P., Frasconi, P., Smyth, P.: Modeling the Internet and the Web, p. 285. Wiley, Chichester (2003)

    Google Scholar 

  9. http://kdd.ics.uci.edu//databases/reuters21578/reuters21578.html

  10. Bao, Y., Tsuchiya, E., Ishii, N., Du, X.: Classification by Instance-Based Learning Algorithm. In: Gallagher, M., Hogan, J.P., Maire, F. (eds.) IDEAL 2005. LNCS, vol. 3578, pp. 133–140. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ishii, N., Murai, T., Yamada, T., Bao, Y. (2006). Classification by Weighting, Similarity and kNN. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_7

Download citation

  • DOI: https://doi.org/10.1007/11875581_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-45485-4

  • Online ISBN: 978-3-540-45487-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics