On label dependence and loss minimization in multi-label classification

Machine Learning

Volumn 88, Issue 1-2, 2012, Pages 5-45

  Dembczyński, Krzysztof ^a   Waegeman, Willem ^b   Cheng, Weiwei ^c   Hüllermeier, Eyke ^c  

^a POZNAN UNIVERSITY OF TECHNOLOGY   (Poland)

^b GHENT UNIVERSITY   (Belgium)

^c PHILIPPS UNIVERSITY MARBURG   (Germany)

Author keywords

Label dependence; Loss functions; Multi label classification

Indexed keywords

ARTIFICIAL INTELLIGENCE; SOFTWARE ENGINEERING;

BINARY RELEVANCES; DECOMPOSITION ALGORITHM; DESIGNED EXPERIMENTS; LOSS FUNCTIONS; LOSS MINIMIZATION; MULTI LABEL CLASSIFICATION; PREDICTIVE PERFORMANCE; STATE OF THE ART;

CLASSIFICATION (OF INFORMATION);

EID: 84865223006     PISSN: 08856125     EISSN: 15730565     Source Type: Journal    
DOI: 10.1007/s10994-012-5285-8     Document Type: Article

References (43)

1. Amit, Y., Dekel, O., & Singer, Y. (2007). A boosting algorithm for label covering in multilabel problems. In JMLR W&P (Vol. 2, pp. 27-34).
2. an der Merwe, A., & Zidek, J. (1980). Multivariate regression analysis and canonical variates. Canadian Journal of Statistics, 8, 27-39.
3. Boutell, M., Luo, J., Shen, X., & Brown, C. (2004). Learning multi-label scene classification. Pattern Recognition, 37(9), 1757-1771. (Pubitemid 38804465)
4. Breiman, L., & Friedman, J. (1997). Predicting multivariate responses in multiple linear regression. Journal of the Royal Statistical Society. Series B. Statistical Methodology, 69, 3-54. (Pubitemid 127686939)
5. Caruana, R. (1997). Multitask learning: A knowledge-based source of inductive bias. Machine Learning, 28, 41-75.
6. Cheng, W., & Hüllermeier, E. (2009). Combining instance-based learning and logistic regression for multilabel classification. Machine Learning, 76(2-3), 211-225.
7. Chierichetti, F., Kumar, R., Pandey, S., & Vassilvitskii, S. (2010). Finding the Jaccard median. In ACM-SIAM SODA 2010 (pp. 293-311).
8. Dekel, O., Manning, C., & Singer, Y. (2004). Log-linear models for label ranking. In S. Thrun, L. Saul, & B. Schölkopf (Eds.), NIPS 16. Cambridge: MIT Press.
9. Dembczy'nski, K., Kotłowski, W., & Słowi'nski, R. (2008). Maximum likelihood rule ensembles. In ICML 2008 (pp. 224-231). Madison: Omnipress.
10. Dembczy'nski, K., Cheng, W., & Hüllermeier, E. (2010a). Bayes optimal multilabel classification via probabilistic classifier chains. In ICML 2010. Madison: Omnipress.
11. Dembczy'nski, K., Waegeman, W., Cheng, W., & Hüllermeier, E. (2010b). On label dependence in multilabel classification. In Second international workshop on learning from multi-label data (MLD 2010), in conjunction with ICML/COLT 2010.
12. Dembczy'nski, K., Waegeman, W., Cheng, W., & Hüllermeier, E. (2010c). Regret analysis for performance metrics in multi-label classification: The case of hamming and subset zero-one loss. In ECML/PKDD 2010. Berlin: Springer.
13. Dembczy'nski, K., Waegeman, W., Cheng, W., & Hüllermeier, E. (2012) An exact algorithm for F-measure maximization. In Advances in neural information processing systems (Vol. 25).
14. Elisseeff, A., & Weston, J. (2002). A kernel method for multi-labelled classification. In NIPS 14 (pp. 681-688).
15. Finley, T., & Joachims, T. (2008). Training structural SVMs when exact inference is intractable. In ICML 2008. Madison: Omnipress.
16. Fürnkranz, J., Hüllermeier, E., Loza Mencía, E., & Brinker, K. (2008). Multilabel classification via calibrated label ranking. Machine Learning, 73(2).
17. Ghamrawi, N., & McCallum, A. (2005). Collective multi-label classification. In CIKM 2005 (pp. 195-200). (Pubitemid 44022130)
18. Godbole, S., & Sarawagi, S. (2004). Discriminative methods for multi-labeled classification. In PAKDD 2004 (pp. 22-30). (Pubitemid 38824880)
19. Hariharan, B., Zelnik-Manor, L., Vishwanathan, S., & Varma,M. (2010). Large scale max-margin multi-label classification with priors. In ICML 2010. Berlin: Omnipress.
20. Hastie, T., Tibshirani, R., & Friedman, J. H. (2007). Elements of statistical learning: data mining, inference, and prediction (2nd ed.). Berlin: Springer.
21. Hsu, D., Kakade, S., Langford, J., & Zhang, T. (2009). Multi-label prediction via compressed sensing. In NIPS 22 (pp. 772-780).
22. Hüllermeier, E., Fürnkranz, J., Cheng, W., & Brinker, K. (2008). Label ranking by learning pairwise preferences. Artificial Intelligence, 172(16-17), 1897-1916.
23. Izenman, A. (1975). Reduced-rank regression for the multivariate linear model. Journal of Multivariate Analysis, 5, 248-262.
24. Joe, H. (2000). Multivariate models and dependence concepts. London: Chapman & Hall.
25. Jordan, M. I. (Ed.) (1998). Learning in graphical models. Dordrecht: Kluwer Academic.
26. Karush, W. (1939). Minima of functions of several variables with inequalities as side constraints. Master's thesis, Dept. of Mathematics, Univ. of Chicago.
27. Kuhn, H. W., & Tucker, A. W. (1951). Nonlinear programming. In Proceedings of the second Berkeley symposium on mathematical statistics and probability (pp. 481-492).
28. Lafferty, J. D., McCallum, A., & Pereira, F. C. N. (2001). Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In ICML 2001 (pp. 282-289).
29. Pestian, J. P., Brew, C., Matykiewicz, P., Hovermale, D. J., Johnson, N., Cohen, K. B., & Duch, W. (2007). A shared task involving multi-label classification of clinical free text. In BioNLP'07: proceedings of the workshop on BioNLP 2007 (pp. 97-104). Association for Computational Linguistics.
30. Pletscher, P., Ong, C. S., & Buhmann, J. M. (2010). Entropy and margin maximization for structured output learning. In ECML/PKDD 2010. Berlin: Springer.
31. Read, J., Pfahringer, B., Holmes, G., & Frank, E. (2009). Classifier chains for multi-label classification. In ECML/PKDD 2009 (pp. 254-269).
32. Schapire, RE, & Singer, Y. (2000). Boostexter: A boosting-based system for text categorization. Machine Learning, 39, 135-168. (Pubitemid 30594821)
33. Sklar, A. (1959). Functions de répartitions à n dimensions et leurs marges (Tech. rep.). Public Institute of Statistics of the University of Paris 8.
34. Tai, F., & Lin, H. T. (2010).Multi-label classification with principle label space transformation. In: Second international workshop on learning from multi-label data, (MLD 2010), in conjunction with ICML/COLT 2010.
35. Trohidis, K., Tsoumakas, G., Kalliris, G., & Vlahavas, I. (2008). Multi-label classification of music into emotions. In ISMIR 2008 (pp. 325-330).
36. Tsochantaridis, Y., Joachims, T., Hofmann, T., & Altun, Y. (2005). Large margin methods for structured and independent output variables. Journal of Machine Learning Research, 6, 1453-1484.
37. Tsoumakas, G., & Katakis, I. (2007). Multi label classification: An overview. International Journal of Data Warehousing and Mining, 3(3), 1-13.
38. Tsoumakas, G. Vlahavas, I. (2007). Random k-labelsets: An ensemble method for multilabel classification. n ECML 2007 (pp. 406-417).
39. Tsoumakas, G., Katakis, I., & Vlahavas, I. (2010). Mining multi-label data. In O. Maimon & L. Rokach (Eds.), Data mining and knowledge discovery handbook. Berlin: Springer.
40. Weston, J., Chapelle, O., Elisseeff, A., Schölkopf, B., & Vapnik, V. (2002). Kernel dependency estimation. In NIPS 2002 (pp. 873-880).
41. Witten, I. H., & Frank, E. (2005). Data mining: practical machine learning tools and techniques (2nd ed.). San Mateo: Morgan Kaufmann.
42. Yu, S., Yu, K., Tresp, V, & Kriegel, H. P. (2006). Multi-output regularized feature projection. IEEE Transactions on Knowledge and Data Engineering, 18(12), 1600-1613. (Pubitemid 44711720)
43. Zhang, M. L., & Zhang, K. (2010).Multi-label learning by exploiting label dependency. In Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 999-1008). New York: ACM.