Classification of nonlinearly separable data by nonlinear support vector machines is often a difficult task especially due to the necessity of a choosing a convenient kernel type. In this study, we propose a new classification method called support vector selection and adaptation (SVSA) that is applicable to both linearly and nonlinearly separable data in terms of some reference vectors generated by processing of support vectors obtained from the linear SVM. The method consists of two steps called selection and adaptation. In these two steps, once the support vectors are obtained by a linear SVM, some of them are rejected and others are selected and adapted to become the reference vectors. Classification is next carried out by using the K Nearest Neighbor Method (KNN) with the reference vectors. In the first step, all support vectors are classified by KNN with respect to the training data excluding the support vectors. The misclassified support vectors are rejected, and the remaining support vectors are chosen as the reference vectors. In the second step, the reference vectors are adapted by moving them towards to or away from the decision boundaries by the Learning Vector Quantization (LVQ) method. At the end of the adaptation process, the reference vectors are finalized. During classification, the class of each input vector is detected with the minimum distance rule in which the distances are calculated from the input vector to all the reference vectors. The SVSA method was experimented with some synthetic and real data, and the experimental results showed that the SVSA is competitive with the traditional SVM.


SVSA, SVM, data classification, remote sensing, linearly and nonlinearly separable data

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