Abstract
Multipartite ranking is a special kind of ranking for problems in which classes exhibit an order. Many applications require its use, for instance, granting loans in a bank, reviewing papers in a conference or just grading exercises in an education environment. Several methods have been proposed for this purpose. The simplest ones resort to regression schemes with a pre- and post-process of the classes, what makes them barely useful. Other alternatives make use of class order information or they perform a pairwise classification together with an aggregation function. In this paper we present and discuss two methods based on building a Decision Directed Acyclic Graph (DDAG). Their performance is evaluated over a set of ordinal benchmark data sets according to the C-Index measure. Both yield competitive results with regard to state-of-the-art methods, specially the one based on a probabilistic approach, called PR-DDAG.
This research has been partially supported by Spanish Ministerio de Ciencia e Innovación (MICINN) grants TIN2007-61273 and TIN2008-06247 and by FICYT, Asturias, Spain, under grant IB09-059-C2.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Brinker, K., Hüllermeier, E.: Case-based label ranking. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) ECML 2006. LNCS (LNAI), vol. 4212, pp. 566–573. Springer, Heidelberg (2006)
Cardoso, J.S., da Costa, J.F.P.: Learning to classify ordinal data: The data replication method. Journal of Machine Learning Research 8, 1393–1429 (2007)
Chen, P., Liu, S.: An improved dag-svm for multi-class classification. International Conference on Natural Computation 1, 460–462 (2009)
Chu, W., Sathiya Keerthi, S.: New approaches to support vector ordinal regression. In: De Raedt, L., Wrobel, S. (eds.) Proceedings of the ICML’05, vol. 119, pp. 145–152. ACM, New York (2005)
Demšar, J.: Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7, 1–30 (2006)
Fawcett, T.: An introduction to roc analysis. Pattern Recognition Letters 27(8), 861–874 (2006)
Feng, J., Yang, Y., Fan, J.: Fuzzy multi-class svm classifier based on optimal directed acyclic graph using in similar handwritten chinese characters recognition. In: Wang, J., Liao, X.-F., Yi, Z. (eds.) ISNN 2005. LNCS, vol. 3496, pp. 875–880. Springer, Heidelberg (2005)
Frank, E., Hall, M.: A simple approach to ordinal classification. In: EMCL ’01: Proceedings of the 12th European Conference on Machine Learning, London, UK, pp. 145–156. Springer, Heidelberg (2001)
Friedman, J.H.: Another approach to polychotomous classification. Technical report, Department of Statistics, Stanford University (1996)
Fürnkranz, J.: Round robin classification. Journal of Machine Learning Research 2, 721–747 (2002)
Fürnkranz, J., Hüllermeier, E., Vanderlooy, S.: Binary decomposition methods for multipartite ranking. In: Buntine, W., Grobelnik, M., Mladenić, D., Shawe-Taylor, J. (eds.) ECML PKDD 2009. LNCS, vol. 5781, pp. 359–374. Springer, Heidelberg (2009)
Gonen, M., Heller, G.: Concordance probability and discriminatory power in proportional hazards regression. Biometrika 92(4), 965–970 (2005)
Hand, D.J., Till, R.J.: A simple generalisation of the area under the roc curve for multiple class classification problems. Machine Learning 45(2), 171–186 (2001)
Herbrich, R., Graepel, T., Obermayer, K.: Large margin rank boundaries for ordinal regression. In: Smola, Bartlett, Schoelkopf, Schuurmans (eds.) Advances in Large Margin Classifiers. MIT Press, Cambridge (2000)
Higgins, J.: Introduction to Modern Nonparametric Statistics. Duxbury Press, Boston (2004)
Hühn, J.C., Hüllermeier, E.: Is an ordinal class structure useful in classifier learning? Int. J. of Data Mining Modelling and Management 1(1), 45–67 (2008)
Hüllermeier, E., Fürnkranz, J., Cheng, W., Brinker, K.: Label ranking by learning pairwise preferences. Artificial Intelligence 172(16-17), 1897–1916 (2008)
Joachims, T.: A support vector method for multivariate performance measures. In: ICML ’05: Proceedings of the 22nd International Conference on Machine Learning, pp. 377–384. ACM, New York (2005)
Kramer, S., Widmer, G., Pfahringer, B., de Groeve, M.: Prediction of ordinal classes using regression trees. In: Ohsuga, S., Raś, Z.W. (eds.) ISMIS 2000. LNCS (LNAI), vol. 1932, pp. 426–434. Springer, Heidelberg (2000)
Li, P., Burges, C.J.C., Wu, Q.: Mcrank: Learning to rank using multiple classification and gradient boosting. In: Platt, J.C., Koller, D., Singer, Y., Roweis, S.T. (eds.) NIPS. MIT Press, Cambridge (2007)
Lin, C.-J., Weng, R.C., Sathiya Keerthi, S.: Trust region newton method for logistic regression. Journal of Machine Learning Research 9, 627–650 (2008)
Luaces, O., Taboada, F., Albaiceta, G.M., Domínguez, L.A., Enríquez, P., Bahamonde, A.: Predicting the probability of survival in intensive care unit patients from a small number of variables and training examples. Artificial Intelligence in Medicine 45(1), 63–76 (2009)
Nguyen, C.D., Dung, T.A., Cao, T.H.: Text classification for dag-structured categories. In: Ho, T.-B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 290–300. Springer, Heidelberg (2005)
Platt, J.C., Cristianini, N., Shawe-taylor, J.: Large margin dags for multiclass classification. In: Advances in Neural Information Processing Systems, pp. 547–553. MIT Press, Cambridge (2000)
Platt, J.C., Platt, J.C.: Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In: Advances in Large Margin Classifiers, pp. 61–74. MIT Press, Cambridge (1999)
Rajaram, S., Agarwal, S.: Generalization bounds for k-partite ranking. In: Agarwal, S., Cortes, C., Herbrich, R. (eds.) Proceedings of the NIPS 2005 Workshop on Learning to Rank, pp. 28–23 (2005)
Takahashi, F., Abe, S.: Optimizing directed acyclic graph support vector machines. In: IAPR - TC3 International Workshop on Artificial Neural Networks in Pattern Recognition University of Florence, Italy (2003)
Vapnik, V., Chapelle, O.: Bounds on error expectation for support vector machines. Neural Computation 12(9), 2013–2036 (2000)
Waegeman, W., De Baets, B., Boullart, L.: Roc analysis in ordinal regression learning. Pattern Recognition Letters 29(1), 1–9 (2008)
Weiss, M.A.: Data structures and algorithm analysis in C, 2nd edn. Addison-Wesley Longman Publishing Co., Inc., Boston (1997)
Wu, T.F., Lin, C.J., Weng, R.C.: Probability estimates for multi-class classification by pairwise coupling. J. of Machine Learning Research 5, 975–1005 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Quevedo, J.R., Montañés, E., Luaces, O., del Coz, J.J. (2010). Adapting Decision DAGs for Multipartite Ranking. In: Balcázar, J.L., Bonchi, F., Gionis, A., Sebag, M. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2010. Lecture Notes in Computer Science(), vol 6323. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15939-8_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-15939-8_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15938-1
Online ISBN: 978-3-642-15939-8
eBook Packages: Computer ScienceComputer Science (R0)