Evaluating an outlier generation method for training tree-based Genetic Programming applied to one-class classification

Genetic Programming (GP) has been successfully applied to supervised classification problems. This work evaluates a tree-based GP implementation in a one-class classification scenario, using artificial outliers generated by a promising method recently developed by Bánhalmi et al. The proposed approach does not require the use of certain techniques employed by related works, thus providing a simpler yet effective strategy for one-class classification based on GP. Experiments presented herein explore parameter sensitivity of Bnhalmi´s outlier generation method and compare the proposed approach to previously published results obtained by others one-class classifiers like υ-SVM, one-class SVM and GMM.