Optimizing the Classification Cost using SVMs with a Double Hinge Loss (Informatica, 2014)
The objective of this study is to minimize the classification cost using Support Vector Machines (SVMs)Classifier with a double hinge loss. Such binary classifiers have the option to reject observations whenthe cost of rejection is lower than that of misclassification. To train this classifier, the standard SVMoptimization problem was modified by minimizing a double hinge loss function considered as a surrogateconvex loss function. The impact of such classifier is illustrated on several discussed results obtained withartificial data and medical data.