TY - JFULL AU - César Estébanez and Ricardo Aler and José M. Valls PY - 2007/8/ TI - Genetic Programming Based Data Projections for Classification Tasks T2 - International Journal of Computer and Information Engineering SP - 2194 EP - 2200 VL - 1 SN - 1307-6892 UR - https://publications.waset.org/pdf/15252 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 7, 2007 N2 - In this paper we present a GP-based method for automatically evolve projections, so that data can be more easily classified in the projected spaces. At the same time, our approach can reduce dimensionality by constructing more relevant attributes. Fitness of each projection measures how easy is to classify the dataset after applying the projection. This is quickly computed by a Simple Linear Perceptron. We have tested our approach in three domains. The experiments show that it obtains good results, compared to other Machine Learning approaches, while reducing dimensionality in many cases. ER -