WS-TWSVM: Weighted Structural Twin Support Vector Machine by local and global information

Recently many researches have published their papers on training a classifier based on the structural information of data. A Structural Twin Support Vector Machine (S-TWSVM) was proposed to introduce and balance all structural information of both intra-class and inter-class into its optimization problems. In fact, this method neither consider the structural information conflicting between clusters of one class nor the noise data points that can influence on the structure of the data distribution. In this paper, we propose a new Weighted Structural Twin Support Vector Machine (WS-TWSVM) by its local and global information. In our proposed method, we use a weighted (rather than a simple) summing of structural information to sufficiently exploit class's distribution information, so that, applying the density information of data points, the effects of the noise points on the data structure can be handled. Thus, our proposed method, WS-TWSVM can fully exploit the prior knowledge more efficiently than S-TWSVM leads to improve the model's generalization capacity. As been shown in the experiments, WS-TWSVM is superior to S-TWSVM in term of classification accuracy.