Evolutionary nonlinear data transformation for visualization and classification tasks

In this paper we propose new approach in data set dimensionality reduction. We use classical principal component analysis transformation. Instead of rejecting features we generate new one by using nonlinear feature transformation. The values of transformation weights are changed evolutionary by using genetic algorithms. Results show better classification rates in smaller feature space. Visualization results also look better.