Enhancing Text Classification by Information Embedded in the Test Set

  • Gabriela Ramírez-de-la-Rosa
  • Manuel Montes-y-Gómez
  • Luis Villaseńor-Pineda
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6008)


Current text classification methods are mostly based on a supervised approach, which require a large number of examples to build models accurate. Unfortunately, in several tasks training sets are extremely small and their generation is very expensive. In order to tackle this problem in this paper we propose a new text classification method that takes advantage of the information embedded in the own test set. This method is supported on the idea that similar documents must belong to the same category. Particularly, it classifies the documents by considering not only their own content but also information about the assigned category to other similar documents from the same test set. Experimental results in four data sets of different sizes are encouraging. They indicate that the proposed method is appropriate to be used with small training sets, where it could significantly outperform the results from traditional approaches such as Naive Bayes and Support Vector Machines.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Han, E.H., Karypis, G.: Centroid-based document classification: Analysis and experimental results. In: Zighed, D.A., Komorowski, J., Żytkow, J.M. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 424–431. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  2. 2.
    Derivaux, S., Forestier, G., Wemmert, C.: Improving supervised learning with multiple clusterings. In: Workshop on Supervised and Unsupervised Ensemble Methods and Their Applications in conjunction with ECAI, Patras, Greece, pp. 57–60 (2008)Google Scholar
  3. 3.
    Cong, G., Lee, W.S., Wu, H., Liu, B.: Semi-supervised text classification using partitioned em. In: Lee, Y., Li, J., Whang, K.-Y., Lee, D. (eds.) DASFAA 2004. LNCS, vol. 2973, pp. 229–239. Springer, Heidelberg (2004)Google Scholar
  4. 4.
    Nigam, K., Mccallum, A., Thrun, S., Mitchell, T.: Text classification from labeled and unlabeled documents using em. In: Machine Learning, 103–134 (1999)Google Scholar
  5. 5.
    Kyriakopoulou, A., Kalamboukis, T.: Using clustering to enhance text classification. In: SIGIR 2007: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 805–806. ACM, New York (2007)CrossRefGoogle Scholar
  6. 6.
    Fang, Y.C., Parthasarathy, S., Schwartz, F.: Using clustering to boost text classification. In: Workshop on Text Mining, TextDM 2001 (2001)Google Scholar
  7. 7.
    Lertnattee, V., Theeramunkong, T.: Term-length normalization for centroid-based text categorization. In: Palade, V., Howlett, R.J., Jain, L. (eds.) KES 2003. LNCS, vol. 2774, pp. 850–856. Springer, Heidelberg (2003)Google Scholar
  8. 8.
    Guan, H., Zhou, J., Guo, M.: A class-feature-centroid classifier for text categorization. In: WWW 2009: Proceedings of the 18th international conference on World wide web, pp. 201–210. ACM, New York (2009)CrossRefGoogle Scholar
  9. 9.
    Cardoso-Cachopo, A., Oliveira, A.L.: Semi-supervised single-label text categorization using centroid-based classifiers. In: SAC 2007: Proceedings of the, ACM symposium on Applied computing, pp. 844–851. ACM, New York (2007)CrossRefGoogle Scholar
  10. 10.
    Tan, S.: An improved centroid classifier for text categorization. Expert Systems with Applications 35, 279–285 (2008)CrossRefGoogle Scholar
  11. 11.
    Lewis, D.D.: Naive (bayes) at forty: The independence assumption in information retrieval, pp. 4–15. Springer, Heidelberg (1998)Google Scholar
  12. 12.
    Joachims, T.: Text categorization with support vector machines: Learning with many relevant features, pp. 137–142. Springer, Heidelberg (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Gabriela Ramírez-de-la-Rosa
    • 1
  • Manuel Montes-y-Gómez
    • 1
  • Luis Villaseńor-Pineda
    • 1
  1. 1.Laboratory of Language TechnologiesNational Institute of Astrophysics, Optics and ElectronicsPue.Mexico

Personalised recommendations