Metrics to guide a multi-objective evolutionary algorithm for ordinal classification

Neurocomputing

Volumn 135, Issue , 2014, Pages 21-31

  Cruz Ramirez M ^a   Hervas Martinez C ^a   Sanchez Monedero J ^a   Gutierrez P A ^a  

^a UNIVERSITY OF CÓRDOBA   (Spain)

Author keywords

Mean absolute error; Multi objective evolutionary algorithm; Ordinal classification; Ordinal measures; Ordinal regression; Proportional odds model

Indexed keywords

ERRORS; MULTIOBJECTIVE OPTIMIZATION; NEURAL NETWORKS; REGRESSION ANALYSIS;

MEAN ABSOLUTE ERROR; MULTI OBJECTIVE EVOLUTIONARY ALGORITHMS; ORDINAL CLASSIFICATION; ORDINAL MEASURE; ORDINAL REGRESSION; PROPORTIONAL ODDS MODEL;

EVOLUTIONARY ALGORITHMS;

ANALYTICAL ERROR; ARTICLE; ARTIFICIAL NEURAL NETWORK; CLASSIFICATION; CLASSIFIER; EVOLUTIONARY ALGORITHM; INTERMETHOD COMPARISON; MATHEMATICAL ANALYSIS; MAXIMUM MEAN ABSOLUTE ERROR; MULTI OBJECTIVE EVOLUTIONARY ALGORITHM; ORDINAL CLASSIFICATION; PRIORITY JOURNAL; SUPPORT VECTOR MACHINE;

EID: 84897913262     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2013.05.058     Document Type: Article

References (38)

1. Hodge R.W., Donald J.T. Class identification in the United States. Am. J. Sociol. 1968, 73:535-547.
2. W. Chu, S.S. Keerthi, New approaches to support vector ordinal regression, in: Proceedings of the 22nd International Conference on Machine Learning, 2005, pp. 145-152.
3. Spearman C. The proof and measurement of association between two things. Am. J. Psychol. 1904, 15:72-101.
4. Kendall M.G. Rank Correlation Methods 1962, Hafner Press, New York.
5. Goodman L., Kruskal W. Measures of association for cross classifications. J. Am. Stat. Assoc. 1954, 49:732-764.
6. Somers R.H. The rank analogue of product-moment partial correlation and regression with application to manifold, ordered contingency tables. Biometrika 1955, 46:241-246.
7. S. Baccianella, A. Esuli, F. Sebastiani, Evaluation measures for ordinal regression, in: Proceedings of the Ninth International Conference on Intelligent Systems Design and Applications, ISDA'09, 2009, pp. 283-287.
8. K. Dembczyński, W. Kotlowski, R. Slowiński, Ordinal classification with decision rules, in: Proceedings of the ECML/PKDD'07 Workshop on Mining Complex Data, Warsaw, PL, 2007, pp. 169-181.
9. Weston J., Bengio S., Usunier N. Large scale image annotation. learning to rank with joint word-image embeddings. Mach. Learn. 2010, 81(1):21-35.
10. Weston J., Bengio S., Hamel P. Multi-tasking with joint semantic spaces for large-scale music annotation and retrieval. J. New Music Res. 2011, 40(4):337-348.
11. Abbass H. An evolutionary artificial neural networks approach for breast cancer diagnosis. Artif. Intell. Med. 2002, 25(3):265-281.
12. Fernández J.C., Martínez F., Hervás C., Gutiérrez P.A. Sensitivity versus accuracy in multi-class problems using memetic Pareto evolutionary neural networks. IEEE Trans. Neural Netw. 2010, 21(5):750-770.
13. Sánchez-Monedero J., Gutiérrez P.A., Fernandez-Navarro F., Hervás-Martínez C. Weighting efficient accuracy and minimum sensitivity for evolving multi-class classifiers. Neural Process. Lett. 2011, 34(2):1370-4621.
14. Cruz-Ramírez M., Hervás-Martínez C., Fernandez-Caballero J., Briceño J., de la Mata M. Multi-objective evolutionary algorithm for donor-recipient decision system in liver transplants. Eur. J. Oper. Res. 2012, 222(2):317-327.
15. Waegeman W., De Baets B., Boullart L. ROC analysis in ordinal regression learning. Pattern Recognit. Lett. 2008, 29(1):1-9.
16. McCullagh P. Regression models for ordinal data. J. R. Stat. Soc. Ser. B (Methodol.) 1980, 42(2):109-142.
17. Storn R., Price K. Differential evolution. a fast and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 1997, 11:341-359.
18. H.A. Abbass, R. Sarker, C. Newton, PDE: a Pareto-frontier differential evolution approach for multi-objective optimization problems, in: Proceedings of the 2001 Congress on Evolutionary Computation, vol. 2, Seoul, South Korea, 2001, pp. 971-978.
19. M. Cruz-Ramírez, C. Hervás-Martínez, J. Sánchez-Monedero, P.A. Gutierrez, A preliminary study of ordinal metrics to guide a multi-objective evolutionary algorithm, in: 11th International Conference on Intelligent Systems Design and Applications, ISDA 2011, Córdoba, Spain, 2011, pp. 1176-1181.
20. Igel C., Hüsken M. Empirical evaluation of the improved Rprop learning algorithms. Neurocomputing 2003, 50(6):105-123.
21. Fleiss J.L., Cohen J., Everitt B.S. Large sample standard errors of kappa and weighted kappa. Psychol. Bull. 1969, 72(5):323-327.
22. Chang C.-C., Lin C.-J. LibSVM. a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2011, 2(3):1-27.
23. Li L., Lin H.T. Ordinal regression by extended binary classification. Adv. Neural Inf. Process. Syst. 2007, 19:865-872.
24. Chu W., Keerthi S.S. Support vector ordinal regression. Neural Comput. 2007, 19(3):792-815.
25. Cardoso J.S., Sousa R. Measuring the performance of ordinal classification. Int. J. Pattern Recognit. Artif. Intell. 2011, 25(8):1173-1195.
26. M. Dorado-Moreno, P. A. Gutiérrez, C. Hervás-Martínez, Ordinal classification using hybrid artificial neural networks with projection and kernel basis functions, in: The Seventh International Conference on Hybrid Artificial Intelligence Systems (HAIS2012), 2012, pp. 319-330.
27. Verwaeren J., Waegeman W., De Baets B. Learning partial ordinal class memberships with kernel-based proportional odds models. Comput. Stat. Data Anal. 2012, 56(4):928-942.
28. Cruz-Ramírez M., Hervás-Martínez C., Gutiérrez P.A., Pérez-Ortiz M., Briceño J., de la Mata M. Memetic Pareto differential evolutionary neural network used to solve an unbalanced liver transplantation problem. Soft Comput. 2012, 17:275-284.
29. J.C. Fernández, C. Hervás, F.J. Martínez, P.A. Gutiérrez, M. Cruz, Memetic Pareto differential evolution for designing artificial neural networks in multiclassification problems using cross-entropy versus sensitivity, in: Proceedings of the Fourth International Conference, HAIS 2009, vol. 5572, Springer-Verlag, Berlin, Heidelberg, 2009, pp. 433-441.
30. Du J.-X., Huang D.-S., Wang X.-F., Gu X. Shape recognition based on neural networks trained by differential evolution algorithm. Neurocomputing 2007, 70(4-6):896-903.
31. Subudhi B., Jena D. Nonlinear system identification using memetic differential evolution trained neural networks. Neurocomputing 2011, 74(10):1696-1709.
32. Cruz-Ramírez M., Hervás-Martínez C., Jurado-Expósito M., López-Granados F. A multi-objective neural network based method for cover crop identification from remote sensed data. Expert Syst. Appl. 2012, 39(11):10038-10048.
33. Bishop C. Pattern Recognition and Machine Learning 2006, Springer.
34. J. Pinto da Costa, J. Cardoso, Classification of ordinal data using neural networks, in: Proceedings of the 16th European Conference on Machine Learning, ECML'05, Springer-Verlag, 2005, pp. 690-697.
35. M. Mathieson, Ordinal models for neural networks, Neural networks in financial engineering, in: Proceedings of the Third International Conference on Neural Networks in the Capital Markets, London, GB, October, 1995, World Scientific, Singapore, 1996, pp. 523-536.
36. Demsar J. Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 2006, 7:1-30.
37. Friedman M. A comparison of alternative tests of significance for the problem of m rankings. Ann. Math. Stat. 1940, 11(1):86-92.
38. Hochberg Y., Tamhane A. Multiple Comparison Procedures 1987, John Wiley & Sons, Inc., New York, NY, USA.