Role of Kernel Parameters in Performance Evaluation of SVM

TLDR: To evaluate the performance of the classifier, the paper has used True positive rate, False Positive rate, Precision, Recall, F-measure and accuracy as performance measures of SVM and evaluated that SVM with linear kernel performs best among all.

Abstract: Identifying performance of classifier is a challenging task. SVM plays an important role in classification. Here different kernel parameters are used as a tuning parameter to improve the classification accuracy. There are mainly four different types of kernels (Linear, Polynomial, RBF, and Sigmoid) that are popular in SVM classifier. The paper presents SVM classification results with above mentioned kernels on two different datasets (Diabetic Retinopathy dataset and Lung Cancer dataset). To evaluate the performance of the classifier we have used True positive rate, False Positive rate, Precision, Recall, F-measure and accuracy as performance measures of SVM. Finally we evaluated that SVM with linear kernel performs best among all.