Behaviour analysis of SVM based spam filtering using various parameter values and accuracy comparison

Abstract

The Increase use of emails generated a need of spam filter. Machine learning algorithm forms a potential method to classify email at a very successful rate. In this paper we will use SVM classifier to classify emails and also note behavior of training and test accuracy with change in parameter C. Informally, the C parameter is a positive value that controls the penalty for misclassified training examples. Description Of algorithm is presented with comparison graph of different values of C to come to a conclusion about high bias and variance.