Scalable multi-output label prediction: From classifier chains to classifier trellises

Pattern Recognition

Volumn 48, Issue 6, 2015, Pages 2096-2109

  Read, Jesse ^a   Martino, Luca ^b   Olmos, Pablo M ^c   Luengo, David ^d  

^a AALTO UNIVERSITY   (Finland)

^b UNIVERSITY OF HELSINKI   (Finland)

^c UNIVERSIDAD CARLOS III DE MADRID   (Spain)

^d UNIVERSIDAD POLITÉCNICA DE MADRID   (Spain)

Author keywords

Bayesian networks; Classifier chains; Multi label classification; Multi output prediction; Structured inference

Indexed keywords

BAYESIAN NETWORKS; FORECASTING;

BENCHMARK DATASETS; CLASSIFIER CHAINS; COMPUTATIONAL BOTTLENECKS; INDIVIDUAL CLASSIFIERS; MULTI LABEL CLASSIFICATION; MULTI-LABEL PROBLEMS; MULTI-OUTPUT; STRUCTURED INFERENCE;

CLASSIFICATION (OF INFORMATION);

EID: 84925368172     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2015.01.004     Document Type: Article

References (27)

1. M.-L. Zhang, and Z.-H. Zhou A review on multi-label learning algorithms IEEE Trans. Knowl. Data Eng. 99 2013 1
2. J.H. Zaragoza, L.E. Sucar, E.F. Morales, C. Bielza, P. Larrañaga, Bayesian chain classifiers for multidimensional classification, in: 24th International Conference on Artificial Intelligence, IJCAI11 (2011).
3. J. Read, L. Martino, and D. Luengo Efficient Monte Carlo methods for multi-dimensional learning with classifier chains Pattern Recognit. 47 2014
4. W. Cheng, K. Dembczyński, E. Hüllermeier, Bayes optimal multilabel classification via probabilistic classifier chains, in: 27th International Conference on Machine Learning, ICML10, Omnipress, Haifa, Israel, 2010.
5. J. Read, B. Pfahringer, G. Holmes, and E. Frank Classifier chains for multi-label classification Mach. Learn. 85 2011 333 359
6. A. Kumar, S. Vembu, A.K. Menon, and C. Elkan Learning and inference in probabilistic classifier chains with beam search P.A. Flach, T. De Bie, N. Cristianini, European Conference on Machine Learning and Knowledge Discovery in Databases (ECML-PKDD) vol. 7523 2012 Springer 665 680
7. M.-L. Zhang, K. Zhang, Multi-label learning by exploiting label dependency, in: 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD10, ACM, 2010, pp. 999-1008.
8. K. Dembczyński, W. Waegeman, W. Cheng, and E. Hüllermeier On label dependence and loss minimization in multi-label classification Mach. Learn. 88 2012 5 45
9. A. Goldenberg, A. Moore, Tractable learning of large Bayes net structures from sparse data, in: Proceedings of the 21st International Conference on Machine learning, ICML04, ACM, New York, NY, USA, 2004, pp. 44.
10. R. Agrawal, T. Imielinski, A. Swami, Mining association rules between sets in items in large databases, in: Proceedings of ACM SIGMOD, vol. 12, pp. 207-216.
11. M. Kalisch, and P. Bühlmann Estimating high-dimensional directed acyclic graphs with the pc-algorithm J. Mach. Learn. Res. 8 2007 613 636
12. R. Yehezkel, and B. Lerner Bayesian network structure learning by recursive autonomy identification J. Mach. Learn. Res. 10 2009 1527 1570
13. A.P. Danyluk, L. Bottou, M.L. Littman (Eds.), Proceedings of the 26th Annual International Conference on Machine Learning, ICML 2009, Montreal, Quebec, Canada, June 14-18, 2009, ACM International Conference Proceeding Series, vol. 382, ACM, 2009.
14. L. Ladick, C. Russell, P. Kohli, P.H.S. Torr, Associative hierarchical crfs for object class image segmentation, in: IEEE 12th International Conference on Computer Vision, 2009, pp. 739-746.
15. N. Ghamrawi, A. McCallum, Collective multi-label classification, in: 14th ACM International Conference on Information and Knowledge Management, CIKM05, ACM Press, New York, NY, USA, 2005, pp. 195-200.
16. D. Barber Bayesian Reasoning and Machine Learning 2012 Cambridge University Press
17. G. Tsoumakas, I.P. Vlahavas, Random k-labelsets: an ensemble method for multilabel classification, in: 18th European Conference on Machine Learning, ECML07, Springer, 2007, pp. 406-417.
18. J. Read, B. Pfahringer, G. Holmes, Multi-label classification using ensembles of pruned sets, in: 8th IEEE International Conference on Data Mining, ICDM08, IEEE, 2008, pp. 995-1000.
19. C. Vens, F. Costa, Random forest based feature induction, in: Proceedings of the 2011 IEEE 11th International Conference on Data Mining, ICDM11, IEEE Computer Society, Washington, DC, USA, 2011, pp. 744-753.
20. H. Su, J. Rousu, Multilabel classification through random graph ensembles, in: Asian Conference on Machine Learning, ACML, pp. 404-418.
21. Y. Guo, S. Gu, Multi-label classification using conditional dependency networks, in: 24th International Conference on Artificial Intelligence, IJCAI/AAAI, IJCAI11, 2011, pp. 1300-1305.
22. T. Hastie, and R. Tibshirani Classification by pairwise coupling M.I. Jordan, M.J. Kearns, S.A. Solla, Advances in Neural Information Processing Systems vol. 10 1998 MIT Press
23. M. Hall, E. Frank, G. Holmes, B. Pfahringer, R. Peter, and I.H. Witten The weka data mining software an update SIGKDD Explor. 11 2009
24. J. Read, B. Pfahringer, G. Holmes, E. Frank, Classifier chains for multi-label classification, in: 20th European Conference on Machine Learning, ECML09, Springer, 2009, pp. 254-269.
25. G. Madjarov, D. Kocev, D. Gjorgjevikj, and S. Deroski An extensive experimental comparison of methods for multi-label learning Pattern Recognit. 45 2012 3084 3104
26. J. Demsař Statistical comparisons of classifiers over multiple data sets J. Mach. Learn. Res. 7 2006 1 30
27. J. Read, K. Achutegui, and J. Miguez A distributed particle filter for nonlinear tracking in wireless sensor networks Signal Process. 98 2014 121 134