Exploitation of pairwise class distances for ordinal classification

Neural Computation

Volumn 25, Issue 9, 2013, Pages 2450-2485

  Sanchez Monedero J ^a   Gutiérrez, Pedro A ^a   Tiňo, Peter ^b   Hervas Martinez C ^a  

^a UNIVERSITY OF CÓRDOBA   (Spain)

^b UNIVERSITY OF BIRMINGHAM   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84885876083     PISSN: 08997667     EISSN: 1530888X     Source Type: Journal    
DOI: 10.1162/NECO_a_00478     Document Type: Letter

References (48)

1. Agresti, A. (1984). Analysis of ordinal categorical data. NewYork: Wiley.
2. Arens, R. (2010). Learning SVM ranking functions from user feedback using document metadata and active learning in the biomedical domain. In J. Fürnkranz & E. Hüllermeier (Eds.), Preference learning (pp. 363-383). New York: Springer-Verlag.
3. Asuncion, A., & Newman, D. (2007). UCI machine learning repository. Irvine, CA: University of California Irvine.
4. Baccianella, S., Esuli, A., & Sebastiani, F. (2009). Evaluation measures for ordinal regression. In Proceedings of the Ninth International Conference on Intelligent Systems Design and Applications (ISDA'09) (pp. 283-287). San Mateo, CA: IEEE Computer Society.
5. Barker, D. (1995). Pasture production data set. Available online at http://www.cs.waikato.ac.nz/ml/weka/datasets.html
6. Cardoso, J. S., & Pinto da Costa, J. F. (2007). Learning to classify ordinal data: The data replication method. Journal of Machine Learning Research, 8, 1393-1429.
7. Cardoso, J., Pinto da Costa, J., & Cardoso, M. (2005). Modelling ordinal relations with SVMs: An application to objective aesthetic evaluation of breast cancer conservative treatment. Neural Networks, 18(5-6), 808-817.
8. Cardoso, J. S., & Sousa, R. (2011). Measuring the performance of ordinal classification. International Journal of Pattern Recognition and Artificial Intelligence, 25(8), 1173-1195.
9. Chang, C.-C., & Lin, C.-J. (2011). LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol., 2, 27:1-27:27.
10. Chu, W., & Ghahramani, Z. (2005). Gaussian processes for ordinal regression. Journal of Machine Learning Research, 6, 1019-1041.
11. Chu, W., & Keerthi, S. S. (2005). Newapproaches to support vector ordinal regression. In In ICML'05: Proceedings of the 22nd international Conference on Machine Learning (pp. 145-152). New York: ACM.
12. Chu, W., & Keerthi, S. S. (2007). Support vector ordinal regression. Neural Computation, 19(3), 792-815.
13. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273-297.
14. Crammer, K., & Singer, Y. (2001). Pranking with ranking. In T. G. Dieterrich, S. Becker, & Z. Ghahramani (Eds.), Advances in neural information processing systems, 14 (pp. 641-647). Cambridge, MA: MIT Press.
15. Crammer, K., & Singer, Y. (2005). Online ranking by projecting. Neural Computation, 17(1), 145-175.
16. Cruz-Ramírez, M., Hervás-Martínez, C., Sánchez-Monedero, J., & Gutiérrez, P. A. (2011). A Preliminary study of ordinal metrics to guide a multi-objective evolutionary algorithm. In Proceedings of the 11th International Conference on Intelligent Systems Design andApplications (ISDA 2011) (pp. 1176-1181). San Mateo, CA: IEEE Computer Society.
17. Demsar, J. (2006). Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res., 7, 1-30.
18. Fouad, S., & Tino, P. (2012). Adaptivemetric learning vector quantization for ordinal classification. Neural Computation, 24(11), 2825-2851.
19. Frank, E., & Hall, M. (2001). Asimple approach to ordinal classification. In Proceedings of the 12th European Conference on Machine Learning (pp. 145-156). New York: Springer-Verlag.
20. Friedman, M. (1940). Acomparison of alternative tests of significance for the problem of m rankings. Annals of Mathematical Statistics, 11(1), 86-92.
21. Gutiérrez, P., Salcedo-Sanz, S., Hervás-Martínez, C., Carro-Calvo, L., Sánchez-Monedero, J., & Prieto, L. (2013). Ordinal and nominal classification of wind speed from synoptic pressure patterns. Engineering Applications of Artificial Intelligence, 26(3), 1008-1015.
22. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The WEKA data mining software: An update. Special Interest Group on Knowledge Discovery and Data Mining Explorer Newsletter, 11, 10-18.
23. Herbrich, R., Graepel, T., & Obermayer, K. (2000). Large margin rank boundaries for ordinal regression. In A. Smola, P. Bartlett, B. Schölkopf, & D. Schuurmans (Eds.), Advances in large margin classifiers (pp. 115-132). Cambridge, MA: MIT Press.
24. Hsu, C.-W., & Lin, C.-J. (2002). A comparison of methods for multi-class support vector machines. IEEE Transactions on Neural Networks, 13(2), 415-425.
25. Hühn, J. C., & Hüllermeier, E. (2008). Is an ordinal class structure useful in classifier learning? Int. J. Data Mining, Modelling and Management, 1(1), 45-67.
26. Kendall, M. G. (1962). Rank correlation methods (3rd ed.). New York: Hafner Press.
27. Kibler, D. F., Aha, D. W., & Albert, M. K. (1989). Instance-based prediction of realvalued attributes. Computational Intelligence, 5, 51.
28. Kim, K.-J., & Ahn, H. (2012). A corporate credit rating model using multi-class support vector machines with an ordinal pairwise partitioning approach. Computers and Operations Research, 39(8), 1800-1811.
29. Kotsiantis, S. B., & Pintelas, P. E. (2004). A cost sensitive technique for ordinal classification problems. In G. Vouros & T. Panayiotopoulos (Eds.), Methods and applications of artificial intelligence (pp. 220-229). Berlin: Springer-Verlag.
30. Kramer, S., Widmer, G., Pfahringer, B., & de Groeve, M. (2010). Prediction of ordinal classes using regression trees. In Z. Ras & S. Ohsuga (Eds.), Foundations of intelligent systems (pp. 665-674). Berlin: Springer-Verlag.
31. Li, L., & Lin, H.-T. (2007). Ordinal regression by extended binary classification. In B. Schölkopf, J. Platt, & T. Hofmann (Eds.), Advances in neural information processing systems, 19 (pp. 865-872). Cambridge, MA: MIT Press.
32. Lim, T.-S., Loh, W.-Y., & Shih, Y.-S. (2000). Acomparison of prediction accuracy, complexity, and training time of thirty-three old and new classification Algorithms. Machine Learning, 40, 203-228.
33. Lin, H.-T., & Li, L. (2012). Reduction from cost-sensitive ordinal ranking to weighted binary classification. Neural Computation, 24(5), 1329-1367.
34. Mackay, D. J. C. (1994). Bayesian methods for backpropagation networks. In E. Domany, J. L. van Hemmen, & K. Schulten (Eds.), Models of neural networks III (pp. 211-254). New York: Springer-Verlag.
35. McCullagh, P. (1980). Regression models for ordinal data. Journal of theRoyal Statistical Society, Series B (Methodological), 42(2), 109-142.
36. Neal, R. M. (1996). Bayesian learning for neural networks. New York: Springer-Verlag.
37. Perez-Ortiz, M., Gutierrez, P. A., Garcia-Alonso, C., Salvador-Carulla, L., Salinas-Perez, J. A., & Hervas-Martinez, C. (2011). Ordinal classification of depression spatial hot-spots of prevalence. In Proceedings of the 11th International Conference on Intelligent Systems Design and Applications (ISDA) (pp. 1170-1175). San Mateo, CA: IEEE Computer Society.
38. Pinto da Costa, J. F., Alonso, H., & Cardoso, J. S. (2008). The unimodal model for the classification of ordinal data. Neural Networks, 21, 78-91.
39. Raykar, V. C., Duraiswami, R., & Krishnapuram, B. (2008). A fast algorithm for learning a ranking function fromlarge-scale data sets. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30, 1158-1170.
40. Sánchez-Monedero, J., Gutiérrez, P. A., Fernández-Navarro, F., & Hervás-Martínez, C. (2011). Weighting efficient accuracy and minimum sensitivity for evolving multiclass classifiers. Neural Processing Letters, 34(2), 101-116.
41. Schölkopf, B., & Smola, A. J. (2001). Learning with kernels: Support vector machines, regularization, optimization, and beyond. Cambridge, MA: MIT Press.
42. Shashua, A., & Levin, A. (2002). Ranking with large margin principle: Two approaches. In S. Becker, S. Thrün, & K. Obermeyer (Eds.), Advances in neural information processing systems, 15 (pp. 937-944). Cambridge, MA: MIT Press.
43. Sonnenburg, D. S. (2011). Machine learning data set repository. http://midata.org
44. Sun, B.-Y., Li, J., Wu, D. D., Zhang, X.-M., & Li, W.-B. (2010). Kernel discriminant learning for ordinal regression. IEEE Transactions on Knowledge and Data Engineering, 22(6), 906-910.
45. Vapnik, V. (1999). An overview of statistical learning theory. IEEE Transactions on Neural Networks, 10(5), 988-999.
46. Verwaeren, J., Waegeman, W., & De Baets, B. (2012). Learning partial ordinal class memberships with kernel-based proportional odds models. Computational Statistics and Data Analysis, 56(4), 928-942.
47. Waegeman, W., & Boullart, L. (2009). An ensemble of weighted support vector machines for ordinal regression. International Journal of Computer Systems Science and Engineering, 3(1), 47-51.
48. Waegeman, W., & De Baets, B. (2011). A survey on ROC-based ordinal regression. In J. Fürnkranz & E. Hüllermeier (Eds.), Preference learning (pp. 127-154). Berlin: Springer-Verlag.