Torgo, L., Ribeiro, R. P., Pfahringer, B. & Branco, P. (2013). SMOTE for regression. In L. Correia, L.P. Reis, and J. Cascalho (Eds.): EPIA 2013, LNAI 8154 (pp. 378-389). Springer-Verlag Berlin Heidelberg.
Permanent Research Commons link: https://hdl.handle.net/10289/8518
Several real world prediction problems involve forecasting rare values of a target variable. When this variable is nominal we have a problem of class imbalance that was already studied thoroughly within machine learning. For regression tasks, where the target variable is continuous, few works exist addressing this type of problem. Still, important application areas involve forecasting rare extreme values of a continuous target variable. This paper describes a contribution to this type of tasks. Namely, we propose to address such tasks by sampling approaches. These approaches change the distribution of the given training data set to decrease the problem of imbalance between the rare target cases and the most frequent ones. We present a modification of the well-known Smote algorithm that allows its use on these regression tasks. In an extensive set of experiments we provide empirical evidence for the superiority of our proposals for these particular regression tasks. The proposed SmoteR method can be used with any existing regression algorithm turning it into a general tool for addressing problems of forecasting rare extreme values of a continuous target variable.
This is an author’s accepted version of an article published in the Proceedings of Progress in Artificial Intelligence: 16th Portuguese Conference on Artificial Intelligence. © Springer International Publishing AG 2016. The final publication is available at Springer via dx.doi.org/10.1007/978-3-642-40669-0_33