Performance evaluation of SVM based semi-supervised classification algorithm

To construct decision boundaries for two-class classification, SVM approach is attractive due to its efficiency. However, this approach is useful for 2-class classification and when the classes (labels) for the data are known. In practice, we have collection of labeled as well as unlabelled data, and it gives rise to semi-supervised classification problem. In this paper, we give a semi-supervised classification algorithm based on support vector machine (SVM). Novel feature of our approach is the formulation of spherical decision boundaries and the exploitation of the dynamical system associated with support function to obtain the number of clusters. The experimental results on a few well-known datasets, namely, Iris dataset, Shuttle landing control dataset, Wisconsin Breast cancer dataset, glass dataset, and balance scale dataset, indicate that our approach results in satisfactory classification as well as generalization accuracy.