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MLG: Enchancing Multi-label Classification with Modularity-Based Label Grouping

  • Piotr Szymański
  • Tomasz Kajdanowicz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8073)

Abstract

Multi-label classification on data sets with large number of labels is a practically viable and intractable problem. This paper presents an optimization method for the multi-label classification process for data with a high number of labels. The newly proposed method starts with label grouping using community detection methods on interconnectedness graph of labels based on support sizes for every pair of labels. The grouping process is based on modularity-oriented community detection methods. Next the data instances are classified separately for each label community and the resulting labellings are merged afterwards. Both theoretical analysis and experimental results are provided. Experimental results comparing common classification methods to proposed Modularity-based Label Grouping (MLG) with embedded Binary Relevance, executed on on differentiated data sets show a performance increase by 27-41% compared to standard binary relevance, by 72-81% compared to RAkel and by several dozens compared to ECOC-BR-BCH with none or negligible difference in classification quality.

Keywords

multi-label classification modularity label grouping community detection label co-occurrence label interconnectedness 

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References

  1. 1.
    Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. ACM SIGMOD Record 22(2), 207–216 (1993)CrossRefGoogle Scholar
  2. 2.
    Newman, M., Girvan, M.: Finding and evaluating community structure in networks. Physical Review E 69(2), 026113 (2004)Google Scholar
  3. 3.
    Tsoumakas, G., Vlahavas, I.P.: Random k-labelsets: An ensemble method for multilabel classification. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) ECML 2007. LNCS (LNAI), vol. 4701, pp. 406–417. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  4. 4.
    Kajdanowicz, T., Kazienko, P.: Multi-label classification using error correcting output codes. International Journal of Applied Mathematics and Computer Science 22(4), 829–840 (2012)CrossRefGoogle Scholar
  5. 5.
    Ghamrawi, N., McCallum, A.: Collective multi-label classification. In: Proceedings of International Conference on Information and Knowledge Management, pp. 195–200. ACM (2005)Google Scholar
  6. 6.
    Godbole, S., Sarawagi, S.: Discriminative methods for multi-labeled classification. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 22–30. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  7. 7.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Information Science and Statistics. Springer-Verlag New York, Inc., Secaucus (2006)zbMATHGoogle Scholar
  8. 8.
    Gibson, D., Kleinberg, J., Raghavan, P.: Inferring Web communities from link topology. In: Proceedings of the Ninth ACM Conference on Hypertext and Hypermedia: Links, Objects, Time and Space—Structure in Hypermedia Systems Links, Objects, Time and Space—Structure in Hypermedia Systems - HYPERTEXT 1998, pp. 225–234. ACM Press, New York (1998)CrossRefGoogle Scholar
  9. 9.
    Newman, M.E.J.: Modularity and community structure in networks. Proceedings of the National Academy of Sciences of the United States of America 103(23), 8577–8582 (2006)CrossRefGoogle Scholar
  10. 10.
    Hofstad, R.V.D.: Random Graphs and Complex Networks (2013), http://www.win.tue.nl/~rhofstad/NotesRGCN.pdf (accessed April 30, 2008)
  11. 11.
    Newman, M.E.J.: Detecting community structure in networks. The European Physical Journal B - Condensed Matter 38(2), 321–330 (2004)CrossRefGoogle Scholar
  12. 12.
    Newman, M.: Fast algorithm for detecting community structure in networks. Physical Review E 69(6), 066133 (2004)Google Scholar
  13. 13.
    Reichardt, J., Bornholdt, S.: Statistical mechanics of community detection. Physical Review E 74(1), 016110 (2006)Google Scholar
  14. 14.
    Michael Hahsler, B.G.: Introduction to arules: Mining Association Rules and Frequent Item SetsGoogle Scholar
  15. 15.
    Pestian, J.P., Brew, C., Matykiewicz, P., Hovermale, D.J., Johnson, N., Cohen, K.B., Duch, W.: A shared task involving multi-label classification of clinical free text. In: Proceedings of the Workshop on BioNLP 2007 Biological Translational and Clinical Language Processing BioNLP 2007, vol. 1, pp. 97–104 (2007)Google Scholar
  16. 16.
    Klimt, B., Yang, Y.: The Enron Corpus: A New Dataset for Email Classification Research. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) ECML 2004. LNCS (LNAI), vol. 3201, pp. 217–226. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  17. 17.
    Diplaris, S., Tsoumakas, G., Mitkas, P.A., Vlahavas, I.: Protein Classification with Multiple Algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence, 448–456 (2005)Google Scholar
  18. 18.
    Kumpula, J.M., Saramäki, J., Kaski, K., Kertész, J.: Limited resolution in complex network community detection with Potts model approach. The European Physical Journal B 56(1), 41–45 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Piotr Szymański
    • 1
    • 2
  • Tomasz Kajdanowicz
    • 1
  1. 1.Faculty of Computer Science and Management, Institute of InformaticsWroclaw University of TechnologyWrocławPoland
  2. 2.illimites FoundationWrocławPoland

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