Blanzieri, Enrico and Bryl, Anton (2007) Evaluation of the Highest Probability SVM Nearest Neighbor Classifier with Variable Relative Error Cost. UNSPECIFIED. (Unpublished)
Abstract
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, using the pure SVM classifier as a quality baseline. To classify a sample the HP-SVM-NN classifier does the following: for each k in a predefined set {k1, ..., 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 two classes using sigmoid approximation; than it selects that of the 2×N resulting answers which has the highest probability. The experimental evaluation shows, that in terms of ROC curves the algorithm is able to achieve higher accuracy than the pure SVM classifier.
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