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.
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References
Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. ACM SIGMOD Record 22(2), 207–216 (1993)
Newman, M., Girvan, M.: Finding and evaluating community structure in networks. Physical Review E 69(2), 026113 (2004)
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)
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)
Ghamrawi, N., McCallum, A.: Collective multi-label classification. In: Proceedings of International Conference on Information and Knowledge Management, pp. 195–200. ACM (2005)
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)
Bishop, C.M.: Pattern Recognition and Machine Learning. Information Science and Statistics. Springer-Verlag New York, Inc., Secaucus (2006)
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)
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)
Hofstad, R.V.D.: Random Graphs and Complex Networks (2013), http://www.win.tue.nl/~rhofstad/NotesRGCN.pdf (accessed April 30, 2008)
Newman, M.E.J.: Detecting community structure in networks. The European Physical Journal B - Condensed Matter 38(2), 321–330 (2004)
Newman, M.: Fast algorithm for detecting community structure in networks. Physical Review E 69(6), 066133 (2004)
Reichardt, J., Bornholdt, S.: Statistical mechanics of community detection. Physical Review E 74(1), 016110 (2006)
Michael Hahsler, B.G.: Introduction to arules: Mining Association Rules and Frequent Item Sets
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)
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)
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)
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)
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Szymański, P., Kajdanowicz, T. (2013). MLG: Enchancing Multi-label Classification with Modularity-Based Label Grouping. In: Pan, JS., Polycarpou, M.M., Woźniak, M., de Carvalho, A.C.P.L.F., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2013. Lecture Notes in Computer Science(), vol 8073. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40846-5_43
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DOI: https://doi.org/10.1007/978-3-642-40846-5_43
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