Evolving training model method for one-class SVM

This paper proposes and analyzes an evolving training model method for selecting the best training parameters of one-class support vector machines (SVM). The method: 1) presents and computes effectively the generalization performance of one-class SVM, including using fraction of support vectors and ξαρ-estimate of recall to evaluate the size of region and the generalization fraction of data points in the region, respectively; and 2) uses genetic algorithms to evolve the training model, the evolution is supervised by the generalization performance of one-class SVM. Experiments on an artificial data illustrate the adaptation of the region to the distribution. Experiments on a standard intrusion detection dataset demonstrate that our method not only improves the false positive rate and detection rate, but also is able to control the tradeoff between these measures.