Multicategory Classification Via Forward-Backward Support Vector Machine

In this paper,we propose a new algorithm to extend support vector machine(SVM)for binary classification to multicategory classification.The proposed method is based on a sequential binary classification algorithm.We first classify a target class by excluding the possibility of labeling as any other classes using a forward step of sequential SVM;we then exclude the already classified classes and repeat the same procedure for the remaining classes in a backward step.The proposed algorithm relies on SVM for each binary classification and utilizes only feasible data in each step;therefore,the method guarantees convergence and entails light computational burden.We prove Fisher consistency of the proposed forward–backward SVM(FB-SVM)and obtain a stochastic bound for the predicted misclassification rate.We conduct extensive simulations and analyze real-world data to demonstrate the superior performance of FB-SVM,for example,FB-SVM achieves a classification accuracy much higher than the current standard for predicting conversion from mild cognitive impairment to Alzheimer’s disease.