Feature generation using genetic programming based on fisher criterion

In this paper, a novel feature extraction method is proposed; Genetic Programming (GP) is used to discover features, while the Fisher criterion is employed to provide fitness values. This produces nonlinear features for both two-class and multi-class recognition problems by revealing the discriminating information between classes. The proposed approach is experimentally compared to conventional nonlinear feature extraction methods, including kernel generalised discriminant analysis (KGDA), kernel principal component analysis (KPCA). Results demonstrate the capability of the proposed approach to transform information from the high dimensional feature space into a single dimensional space by automatically discovering the relationships among data.