Structural twin support vector machine for classification

It has been shown that the structural information of data may contain useful prior domain knowledge for training a classifier. How to apply the structural information of data to build a good classifier is a new research focus recently. As we all know, the all existing structural large margin methods are the common in considering all structural information within classes into one model. In fact, these methods do not balance all structural information’s relationships both infra-class and inter-class, which directly results in these prior information not being exploited sufficiently. In this paper, we design a new Structural Twin Support Vector Machine (called SS-TWSVM). Unlike existing methods based on structural information, SS-TWSVM uses two hyperplanes to decide the category of new data, of which each model only considers one class’s structural information and closer to the class at the same time far away from the other class. This makes SS-TWSVM fully exploit these prior knowledge to directly improve the algorithm’s the capacity of generalization. All experiments show that our proposed method is rigidly superior to the state-of-the-art algorithms based on structural information of data in both computation time and classification accuracy.