Classification of Small Datasets: Why Using Class-Based Weighting Measures?

  • Flavien Bouillot
  • Pascal Poncelet
  • Mathieu Roche
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8502)


In text classification, providing an efficient classifier even if the number of documents involved in the learning step is small remains an important issue. In this paper we evaluate the performance of traditional classification methods to better evaluate their limitation in the learning phase when dealing with small amount of documents. We thus propose a new way for weighting features which are used for classifying. These features have been integrated in two well known classifiers: Class-Feature-Centroid and Naïve Bayes, and evaluations have been performed on two real datasets. We have also investigated the influence on parameters such as number of classes, documents or words in the classification. Experiments have shown the efficiency of our proposal relatively to state of the art classification methods. Either with a very few amount of data or with a small number of features that can be extracted from poor content documents, we show that our approach performs well.


Weighting Measure Small Dataset Inverse Document Frequency Active Learning Method Label Document 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Forman, G., Cohen, I.: Learning from little: Comparison of classifiers given little training. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) PKDD 2004. LNCS (LNAI), vol. 3202, pp. 161–172. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  2. 2.
    Zeng, H.J., Wang, X.H., Chen, Z., Lu, H., Ma, W.Y.: Cbc: Clustering based text classification requiring minimal labeled data. In: Proceedings of the Third IEEE International Conference on Data Mining, ICDM 2003, p. 443 (2003)Google Scholar
  3. 3.
    Lin, F., Cohen, W.W.: Semi-supervised classification of network data using very few labels. In: Proceedings of the 2010 International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2010, pp. 192–199 (2010)Google Scholar
  4. 4.
    Salton, G., McGill, M.J.: Introduction to Modern Information Retrieval. McGraw-Hill, Inc., New York (1986)Google Scholar
  5. 5.
    Guan, H., Zhou, J., Guo, M.: A class-feature-centroid classifier for text categorization. In: Proceedings of the 18th International Conference on World Wide Web, WWW 2009, pp. 201–210. ACM, New York (2009)CrossRefGoogle Scholar
  6. 6.
    Zhang, X., Wang, T., Liang, X., Ao, F., Li, Y.: A class-based feature weighting method for text classification. Journal of Computational Information Systems 8(3), 965–972 (2012)Google Scholar
  7. 7.
    Kim, S.B., Han, K.S., Rim, H.C., Myaeng, S.H.: Some effective techniques for naive bayes text classification. IEEE Transactions on Knowledge and Data Engineering 18(11), 1457–1466 (2006)CrossRefGoogle Scholar
  8. 8.
    Joachims, T.: A statistical learning learning model of text classification for support vector machines. In: Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2001, pp. 128–136. ACM (2001)Google Scholar
  9. 9.
    Lewis, D.D.: Naive (bayes) at forty: The independence assumption in information retrieval. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 4–15. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  10. 10.
    Platt, J.C.: Advances in kernel methods, pp. 185–208. MIT Press, Cambridge (1999)Google Scholar
  11. 11.
    Chang, C.C., Lin, C.J.: Libsvm: A library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 27:1–27:27 (2011)Google Scholar
  12. 12.
    Su, J., Zhang, H., Ling, C.X., Matwin, S.: Discriminative parameter learning for bayesian networks. In: Proceedings of the 25th International Conference on Machine Learning, pp. 1016–1023. ACM (2008)Google Scholar
  13. 13.
    Holmes, G., Pfahringer, B., Kirkby, R., Frank, E., Hall, M.: Multiclass alternating decision trees. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) ECML 2002. LNCS (LNAI), vol. 2430, pp. 161–172. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  14. 14.
    John, G.H., Langley, P.: Estimating continuous distributions in bayesian classifiers. In: Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, pp. 338–345. Morgan Kaufmann Publishers Inc. (1995)Google Scholar
  15. 15.
    McCallum, A., Nigam, K., et al.: A comparison of event models for naive bayes text classification. In: AAAI 1998 Workshop on Learning for Text Categorization, pp. 41–48 (1998)Google Scholar
  16. 16.
    Quinlan, J.R.: C4.5: Programs for machine learning. Morgan Kaufmann Publishers Inc., San Francisco (1993)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Flavien Bouillot
    • 1
    • 2
  • Pascal Poncelet
    • 1
  • Mathieu Roche
    • 1
    • 3
  1. 1.LIRMMUniv. Montpellier 2, CNRSFrance
  2. 2.ITESOFT, AimarguesFrance
  3. 3.TETIS, Cirad, Irstea, AgroParisTechFrance

Personalised recommendations