WEIGHTED STRUCTURAL SUPPORT VECTOR MACHINE

In binary classification problems, two classes of data seem to be different from each other. It is expected more complicated due the clusters in class also tend different. Traditional algorithms as Support Vector Machine (SVM) or Twin (TWSVM) cannot sufficiently exploit structural information with cluster granularity data, cause limitation on capability simulation trends. Structural (S-TWSVM) exploits for learning a represented hyperplane. Therefore, S-TWSVM’s better than that TWSVM. However, datasets where consists trends, seems restricted. Besides, training time S-TWSVM has not been improved compared This paper proposes new Weighted - (called WS-SVM) problems class-vs-clusters strategy. Experimental results show WS-SVM could describe tendency distribution information. Furthermore, both theory and experiment problem significantly S-TWSVM.