A Comparative Study on Term Weighting Schemes for Text Classification

  • Ahmad MazyadEmail author
  • Fabien Teytaud
  • Cyril Fonlupt
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10710)


Text Classification (or Text Categorization) is a popular machine learning task. It consists in assigning categories to documents. In this paper, we are interested in comparing state of the art classifiers and state of the art feature weights. Feature weight methods are classic tools that are used in text categorization. We extend previous studies by evaluating numerous term weighting schemes for state of the art classification methods. We aim at providing a complete survey on text classification for fair benchmark comparisons.


  1. 1.
    Apte, C., Damerau, F., Weiss, S., et al.: Text mining with decision rules and decision trees. Citeseer (1998)Google Scholar
  2. 2.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273–297 (1995). SpringerzbMATHGoogle Scholar
  3. 3.
    Crammer, K., Dekel, O., Keshet, J., Shalev-Shwartz, S., Singer, Y.: Online passive-aggressive algorithms. J. Mach. Learn. Res. 7, 551–585 (2006)MathSciNetzbMATHGoogle Scholar
  4. 4.
    Debole, F., Sebastiani, F.: Supervised term weighting for automated text categorization. In: Sirmakessis, S. (ed.) Text Mining and its Applications. Studies in Fuzziness and Soft Computing, vol. 138, pp. 81–97. Springer, Heidelberg (2004). Scholar
  5. 5.
    Deng, Z.-H., Tang, S.-W., Yang, D.-Q., Li, M.Z.L.-Y., Xie, K.-Q.: A comparative study on feature weight in text categorization. In: Yu, J.X., Lin, X., Lu, H., Zhang, Y. (eds.) APWeb 2004. LNCS, vol. 3007, pp. 588–597. Springer, Heidelberg (2004). Scholar
  6. 6.
    Joachims, T.: Text categorization with support vector machines: learning with many relevant features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS (LNAI), vol. 1398, pp. 137–142. Springer, Heidelberg (1998). Scholar
  7. 7.
    Lan, M., Tan, C.L., Su, J., Lu, Y.: Supervised and traditional term weighting methods for automatic text categorization. IEEE Trans. Pattern Anal. Mach. Intell. 31(4), 721–735 (2009)CrossRefGoogle Scholar
  8. 8.
    McCallum, A., Nigam, K., et al.: A comparison of event models for Naive Bayes text classification. In: Workshop on Learning for Text Categorization, AAAI 1998, vol. 752, pp. 41–48. Citeseer (1998)Google Scholar
  9. 9.
    Ng, H.T., Goh, W.B., Low, K.L.: Feature selection, perceptron learning, and a usability case study for text categorization. In: ACM SIGIR Forum, vol. 31, pp. 67–73. ACM (1997)Google Scholar
  10. 10.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers Inc., San Francisco (1993)Google Scholar
  11. 11.
    Schapire, R.E., Singer, Y.: Boostexter: a boosting-based system for text categorization. Mach. Learn. 39(2–3), 135–168 (2000)CrossRefGoogle Scholar
  12. 12.
    Sparck Jones, K.: A statistical interpretation of term specificity and its application in retrieval. J. Documentation 28(1), 11–21 (1972)CrossRefGoogle Scholar
  13. 13.
    Tibshirani, R., Hastie, T., Narasimhan, B., Chu, G.: Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc. Natl. Acad. Sci. 99(10), 6567–6572 (2002)CrossRefGoogle Scholar
  14. 14.
    Wang, D., Zhang, H.: Inverse category frequency based supervised term weighting scheme for text categorization. Preprint arXiv:1012.2609v4 (2013)
  15. 15.
    Wu, X., Kumar, V., Quinlan, J.R., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G.J., Ng, A., Liu, B., Philip, S.Y., et al.: Top 10 algorithms in data mining. Knowl. Inf. Syst. 14(1), 1–37 (2008)CrossRefGoogle Scholar
  16. 16.
    Yang, Y.: Expert network: effective and efficient learning from human decisions in text categorization and retrieval. In: Croft, B.W., van Rijsbergen, C.J. (eds.) SIGIR 1994, pp. 13–22. Springer, Cham (1994). Scholar
  17. 17.
    Youngjoong, K.: A study of term weighting schemes using class information for text classification. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1029–1030. ACM (2012)Google Scholar
  18. 18.
    Zhang, T.: Solving large scale linear prediction problems using stochastic gradient descent algorithms. In: Proceedings of the Twenty-first International Conference on Machine Learning, ICML 2004, pp. 919–926. Omnipress (2004)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.LISIC, Université du Littoral Côte d’OpaleCalaisFrance

Personalised recommendations