Multilabel classification through random graph ensembles

Machine Learning

Volumn 99, Issue 2, 2015, Pages 231-256

  Su, Hongyu ^a   Rousu, Juho ^a  

^a AALTO UNIVERSITY   (Finland)

Author keywords

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

Indexed keywords

ADAPTIVE BOOSTING; ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); GRAPH THEORY; GRAPHIC METHODS;

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

LEARNING SYSTEMS;

EID: 84928376670     PISSN: 08856125     EISSN: 15730565     Source Type: Journal    
DOI: 10.1007/s10994-014-5465-9     Document Type: Article

References (24)

1. Argyriou, A., Evgeniou, T., & Pontil, M. (2008). Convex multi-task feature learning. Machine Learning, 73(3), 243–272.
2. Bian, W., Xie, B., & Tao, D. (2012). Corrlog: Correlated logistic models for joint prediction of multiple labels. In: Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics (AISTATS-12), vol. 22, pp. 109–117.
3. Breiman, L. (1996). Bagging predictors. Machine Learning, 24, 123–140.
4. Brown, G., & Kuncheva, LI. (2010). Good and bad diversity in majority vote ensembles. In: Multiple classifier systems (pp. 124–133). Berlin: Springer.
5. Cristianini, N., & Shawe-Taylor, J. (2000). An introduction to support vector machines and other kernel-based learning methods (1st ed.). Cambridge: Cambridge University Press.
6. Demšar, J. (2006). Statistical comparisons of classifiers over multiple data sets. The Journal of Machine Learning Research, 7, 1–30.
7. Dunn, O. J. (1961). Multiple comparisons among means. Journal of the American Statistical Association, 56(293), 52–64.
8. Esuli, A., Fagni, T., & Sebastiani, F. (2008). Boosting multi-label hierarchical text categorization. Information Retrieval, 11(4), 287–313.
9. Freund, Y., & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.
10. Friedman, M. (1937). The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical Association, 32(200), 675–701.
11. Krogh, A., & Vedelsby, J. (1995). Neural network ensembles, cross validation, and active learning. In: Advances in neural information processing systems (pp. 231–238). Cambridge, MA: MIT Press.
12. Rajaraman, A., & Ullman, J. (2011). Mining of massive datasets. Cambridge: Cambridge University Press.
13. Ralaivola, L., Swamidass, S., Saigo, H., & Baldi, P. (2005). Graph kernels for chemical informatics. Neural Networks, 18, 1093–1110.
14. Rousu, J., Saunders, C., Szedmak, S., & Shawe-Taylor, J. (2006). Kernel-based learning of hierarchical multilabel classification models. The Journal of Machine Learning Research, 7, 1601–1626.
15. Rousu, J., Saunders, C., Szedmak, S., & Shawe-Taylor, J. (2007). Efficient algorithms for max-margin structured classification. In Predicting structured data, pp. 105–129.
16. Schapire, R., & Singer, Y. (1998). Improved boosting algorithms using confidence-rated predictions. In: Proceedings of the Annual Conference on Computational Learning Theory (pp. 80–91). New York: ACM Press.
17. Schapire, R. E., & Singer, Y. (2000). Boostexter: A boosting-based system for text categorization. Machine Learning, 39(2/3), 135–168.
18. Schölkopf, B., & Smola, A. (2001). Learning with Kernels. Cambridge, MA: MIT Press.
19. Su, H., & Rousu, J. (2001). Multi-task drug bioactivity classification with graph labeling ensembles. In Proceedings of the 6th International Conference on Pattern Recognition in Bioinformatics (PRIB2011), Lecture Note in Computer Science (LNCS), (Vol. 7035, pp.157–167).
20. Su, H., & Rousu, J. (2013). Multilabel classification through random graph ensembles. In: Proceedings, 5th Asian conference on machine learning (ACML2013), Journal of Machine Learning Research W&CP, vol. 29, pp. 404–418.
21. Taskar, B., Guestrin, G., & Koller, D. (2004). Max-Margin Markov networks. In S. Thrun, L. K. Saul, & B. Schölkopf (Eds.), Advances in neural information processing systems, (Vol. 16, pp. 25–32). MIT Press.
22. Tsochantaridis, I., Hofmann, T., Joachims, T., & Altun, Y. (2004). Support vector machine learning for interdependent and structured output spaces. In: Proceedings of the twenty-first international conference on machine learning ICML’04, pp. 823–830.
23. Wainwright, M., Jaakkola, T., & Willsky, A. (2005). MAP estimation via agreement on trees: message-passing and linear programming. IEEE Transactions on Information Theory, 51(11), 3697–3717.
24. Yan, R., Tesic, J., & Smith, J. (2007). Model-shared subspace boosting for multi-label classification. In: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, (pp. 834–843). ACM