Abstract
In classification problems, a dataset is said to be imbalanced when the distribution of the target variable is very unequal. Classes contribute unequally to the decision boundary, and special metrics are used to evaluate these datasets. In previous work, we presented pairwise ranking as a method for binary imbalanced classification, and extended to the ordinal case using weights. In this work, we extend ordinal classification using traditional balancing methods. A comparison is made against traditional and ordinal SVMs, in which the ranking adaption proposed is found to be competitive.
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References
Cruz, R., Fernandes, K., Cardoso, J.S., Pinto Costa, J.F.: Tackling class imbalance with ranking. In: International Joint Conference on Neural Networks (IJCNN). IEEE (2016)
Cruz, R., Fernandes, K., Pinto Costa, J.F., Perez Ortiz, M., Cardoso, J.S.: Ordinal class imbalance with ranking. In: Rojas, I., et al. (eds.) IWANN 2017, Part II. LNCS, vol. 10306, pp. 538–548. Springer, Cham (2017)
Cardoso, J.S., Costa, J.F.: Learning to classify ordinal data: the data replication method. J. Mach. Learn. Res. 8(Jul), 1393–1429 (2007)
Chu, W., Sathiya Keerthi, S.: New approaches to support vector ordinal regression. In: Proceedings of the 22nd International Conference on Machine Learning, pp. 145–152. ACM (2005)
Herbrich, R., Graepel, T., Obermayer, K.: Support vector learning for ordinal regression. In: Ninth International Conference on Artificial Neural Networks, ICANN 1999, (Conf. Publ. No. 470), vol. 1, pp. 97–102. IET (1999)
Pinto Costa, J.F., Sousa, R., Cardoso, J.S.: An all-at-once unimodal SVM approach for ordinal classification. In: Ninth International Conference on Machine Learning and Applications (ICMLA), pp. 59–64. IEEE (2010)
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)
Pérez-Ortiz, M., Gutiérrez, P.A., Hervás-Martínez, C., Yao, X.: Graph-based approaches for over-sampling in the context of ordinal regression. IEEE Trans. Knowl. Data Eng. 27(5), 1233–1245 (2015)
Liu, X.-Y., Jianxin, W., Zhou, Z.-H.: Exploratory undersampling for class imbalance learning. IEEE Trans. Syst. Man Cybern. 39(2), 539–550 (2009)
Sahare, M., Gupta, H.: A review of multi-class classification for imbalanced data. Int. J. Adv. Comput. Res. 2(5), 160–164 (2012)
Hu, S., Liang, Y., Ma, L., He, Y.: MSMOTE: improving classification performance when training data is imbalanced. In: 2nd International Workshop on Computer Science and Engineering, WCSE 2009, vol. 2, pp. 13–17 (2009)
Cruz-Ramírez, M., Hervás-Martínez, C., Sánchez-Monedero, J., Gutiérrez, P.A.: Metrics to guide a multi-objective evolutionary algorithm for ordinal classification. Neurocomputing 135, 21–31 (2014)
Lichman, M.: UCI Machine Learning Repository (2013). http://archive.ics.uci.edu/ml
PASCAL. Pascal (pattern analysis, statistical modelling and computational learning) machine learning benchmarks repository (2011). http://mldata.org/
Chu, W., Ghahramani, Z.: Gaussian processes for ordinal regression. J. Mach. Learn. Res. 6(Jul), 1019–1041 (2005)
Acknowledgment
This work was funded by the Project “NanoSTIMA: Macro-to-Nano Human Sensing: Towards Integrated Multimodal Health Monitoring and Analytics/NORTE-01-0145-FEDER-000016” financed by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF), and also by Fundação para a Ciência e a Tecnologia (FCT) within PhD grant numbers SFRH/BD/122248/2016 and SFRH/BD/93012/2013.
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Cruz, R., Fernandes, K., Pinto Costa, J.F., Pérez Ortiz, M., Cardoso, J.S. (2017). Combining Ranking with Traditional Methods for Ordinal Class Imbalance. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2017. Lecture Notes in Computer Science(), vol 10306. Springer, Cham. https://doi.org/10.1007/978-3-319-59147-6_46
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DOI: https://doi.org/10.1007/978-3-319-59147-6_46
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