Advertisement

A Blending of Simple Algorithms for Topical Classification

  • Alexander D’yakonov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7413)

Abstract

Algorithm which has taken the third place in “JRS 2012 Data Mining Competition” among 126 participants is described. The competition was related to the problem of predicting topical classification of scientific publications in a field of biomedicine. The presented algorithm is a combination (blend) of simple classification algorithms: a linear classifier, a k-NN classifier and two SVMs. We build the combination using special estimation matrices. It proves again that combinations have significantly better performance compared to their individual members.

Keywords

topical classification blending simple algorithms text classification SVD SVM k-NN linear classifier 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
  2. 2.
    National Library of Medicine: PubMed Central (PMC): An Archive for Literature from Life Sciences Journals. In: McEntyre, J., Ostell, J. (Eds.): The NCBI Handbook, http://www.ncbi.nlm.nih.gov/books/NBK21087/
  3. 3.
    National Library of Medicine: Introduction to MeSH (2012), http://www.nlm.nih.gov/mesh/introduction.html
  4. 4.
    van Rijsbergen, C.J.: Information Retrieval, 2nd edn. Butterworth (1979)Google Scholar
  5. 5.
    Bauer, E., Kohavi, R.: An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants. Machine Learning 36(1-2), 105–139 (1999)CrossRefGoogle Scholar
  6. 6.
    Breiman, L.: Bagging predictors. Machine Learning 24, 123–140 (1996)MathSciNetzbMATHGoogle Scholar
  7. 7.
    Freund, Y., Schapire, R.E.: A short introduction to boosting. Journal of Japanese Society for Artificial Intelligence 14(5), 771–780 (1999)Google Scholar
  8. 8.
    Zhuravlev, Y.I.: An Algebraic Approach to Recognition and Classification Problems. In: Problems of Cybernetics, vol. 33, pp. 5–68. Nauka, Moscow (1978); Hafner (1986)Google Scholar
  9. 9.
    Zhuravlev, Yu, I.: Correct Algorithms over Sets of Incorrect (Heuristic) Algorithms: Part II. Kibernetika 6, 21–27 (1977)Google Scholar
  10. 10.
    D’yakonov, A.: Two Recommendation Algorithms Based on Deformed Linear Combinations. In: Proc. of ECML-PKDD 2011 Discovery Challenge Workshop, pp. 21–28 (2011), http://ceur-ws.org/Vol-770/paper5.pdf
  11. 11.
  12. 12.
    Manning, C., Raghavan, P., Schutze, H.: Introduction to Information Retrieval. Cambridge University Press (2008)Google Scholar
  13. 13.
    http://en.wikipedia.org/wiki/Cross-validation_(statistics)Google Scholar
  14. 14.
    Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2(3) (2011), http://www.csie.ntu.edu.tw/~cjlin/libsvm
  15. 15.
    Cortes, C., Vapnik, V.: Support-Vector Networks. Machine Learning, 20 (1995)Google Scholar
  16. 16.
  17. 17.
    Fan, R.-E., Chang, K.-W., Hsieh, C.-J., Wang, X.-R., Lin, C.-J.: LIBLINEAR: A Library for Large Linear Classification. Journal of Machine Learning Research 9, 1871–1874 (2008), http://www.csie.ntu.edu.tw/~cjlin/liblinear zbMATHGoogle Scholar
  18. 18.
    Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice Hall (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Alexander D’yakonov
    • 1
  1. 1.Moscow State UniversityMoscowRussia

Personalised recommendations