Multilabel classification through random graph ensembles

Journal of Machine Learning Research

Volumn 29, Issue , 2013, Pages 404-418

  Su, Hongyu ^a   Rousu, Juho ^a  

^a AALTO UNIVERSITY   (Finland)

Author keywords

Ensemble methods; Graphical models; Kernel methods; Multilabel classification; Structured output

Indexed keywords

ENSEMBLE METHODS; GRAPHICAL MODEL; KERNEL METHODS; MULTI-LABEL CLASSIFICATIONS; STRUCTURED OUTPUT;

EID: 84908472806     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (14)

1. Andreas Argyriou, Theodoros Evgeniou, and Massimiliano Pontil. Multi-task feature learning. In Advances in Neural Information Processing Systems 19. MIT Press, 2007.
2. Wei Bian, Bo Xie, and Dacheng Tao. Corrlog: Correlated logistic models for joint prediction of multiple labels. In Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics (AISTATS-12), volume 22, pages 109-117, 2012.
3. Leo Breiman. Bagging predictors. Machine Learning, 24:123-140, 1996.
4. Gavin Brown and Ludmila I Kuncheva. Good and bad diversity in majority vote ensembles. In Multiple Classifier Systems, pages 124-133. Springer, 2010.
5. A. Esuli, T. Fagni, and F. Sebastiani. Boosting multi-label hierarchical text categorization. Information Retrieval, 11(4):287-313, 2008.
6. Anders Krogh and Jesper Vedelsby. Neural network ensembles, cross validation, and active learning. In Advances in Neural Information Processing Systems, pages 231-238. MIT Press, 1995.
7. J. Rousu, C. Saunders, S. Szedmak, and J. Shawe-Taylor. Kernel-Based Learning of Hierarchical Multilabel Classification Models. The Journal of Machine Learning Research, 7: 1601-1626, 2006.
8. J. Rousu, C. Saunders, S. Szedmak, and J. Shawe-Taylor. Efficient algorithms for maxmargin structured classification. Predicting Structured Data, pages 105-129, 2007.
9. Robert E. Schapire and Yoram Singer. Boostexter: A boosting-based system for text categorization. Machine Learning, 39(2/3):135 - 168, 2000.
10. H. Su and J. Rousu. Multi-task drug bioactivity classification with graph labeling ensembles. Pattern Recognition in Bioinformatics, pages 157-167, 2011.
11. B. Taskar, C. Guestrin, and D. Koller. Max-margin markov networks. In Neural Information Processing Systems, 2003.
12. I. Tsochantaridis, T. Hofmann, T. Joachims, and Y. Altun. Support vector machine learning for interdependent and structured output spaces. In ICML'04, pages 823-830, 2004.
13. M.J. Wainwright, T.S. Jaakkola, and A.S. Willsky. MAP estimation via agreement on trees: message-passing and linear programming. IEEE Transactions on Information Theory, 51 (11):3697-3717, 2005.
14. R. Yan, J. Tesic, and J.R. Smith. Model-shared subspace boosting for multi-label classification. In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 834-843. ACM, 2007.