Distance-weighted Support Vector Machine

• DOI: 10.4310/sii.2015.v8.n3.a7
• Publication Date: 2015-04-17T22:11:47Z
• Authors: Xingye Qiao; Lingsong Zhang
• Keywords: Overfitting; Hinge loss; Interpretability; Hyperplane

A novel linear classification method that possesses the merits of both Support Vector Machine (SVM) and Distance-weighted Discrimination (DWD) is proposed in this article.The can be viewed as a hybrid SVM DWD finds direction by minimizing mainly loss, determines intercept term manner.We show our inheres merit DWD, hence, overcomes data-piling overfitting issue SVM.On other hand, new not subject to imbalanced data which was main advantage over DWD.It uses an unusual loss combines Hinge (of SVM) through trick axillary hyperplane.Several theoretical properties, including Fisher consistency asymptotic normality DWSVM solution are developed.We use some simulated examples compete on performance interpretability.A real application further establishes usefulness approach.