Open AccessMulticategory Classification Via Forward–Backward Support Vector Machine
Author(s) -
Xuan Zhou,
Yuanjia Wang,
Donglin Zeng
Publication year - 2019
Publication title -
communications in mathematics and statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.873
H-Index - 14
eISSN - 2194-6701
pISSN - 2194-671X
DOI - 10.1007/s40304-019-00179-2
Subject(s) - support vector machine , binary classification , binary number , pattern recognition (psychology) , artificial intelligence , computer science , structured support vector machine , consistency (knowledge bases) , convergence (economics) , multiclass classification , class (philosophy) , machine learning , mathematics , algorithm , arithmetic , economics , economic growth
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.