Evaluation of the Highest Probability SVM Nearest Neighbor Classifier with Variable Relative Error Cost

In this paper we evaluate the performance of the highest probability SVM nearest neighbor (HP-SVM-NN) classifier, which combines the ideas of the SVM and k-NN classifiers, on the task of spam filtering. To classify a sample, the HP-SVM-NN classifier does the following: for each k in a predefined set {k 1, ., kN} it trains an SVM model on k nearest labeled samples, uses this model to classify the given sample, and transforms the output of SVM into posterior probabilities of the classes using sigmoid approximation; than it selects that of the 2×N resulting answers which has the highest probability. The evaluation shows that in terms of ROC curves the algorithm is able outperform pure SVM.