A pinball loss support vector machine based regression model
Pritam Anand · 2021 IEEE 18th India Council International Conference (INDICON) · 2021
The Pin-SVM model (Huang et al., [1]) has gained popularity among researchers in recent days. Bi and Bennett [2] have developed a geometrical framework, in which they have derived the optimization problem of the SVR model from a related SVM model. In this paper, we have used the result of Bi and Bennett in the Pin-SVM model to obtain its corresponding SVR model. We have termed the resulting model with ‘Pinball loss Support Vector machine-based Regression’ (Pin-SVR) model and briefly described its different properties. Further, we have compared the prediction of the proposed Pin-SVR model with the existing standard $\epsilon$-SVR model and its L1-norm variant on several artificial and real-world benchmark datasets. Our numerical results conclude that the proposed Pin-SVR model can obtain the better generalization ability than the existing $\epsilon$-SVR model and its variant.