One-class learning with multi-objective genetic programming

One-class classification naturally only provides one class of exemplars on which to construct the classification model. In this work, multi-objective genetic programming (GP) allows the one-class learning problem to be decomposed by multiple GP classifiers, each attempting to identify only a subset of the target data to classify. In order for GP to identify appropriate subsets of the one-class data, artificial outclass data is generated in and around the provided inclass data. A local Gaussian wrapper is employed where this reinforces a novelty detection as opposed to a discrimination approach to classification. Furthermore, a hierarchical subset selection strategy is used to deal with the necessarily large number of generated outclass exemplars. The proposed approach is demonstrated on three one-class classification datasets and was found to be competitive with a one-class SVM classifier and a binary SVM classifier.