Classification by means of Evolutionary Response Surfaces

Abstract. Response surfaces are a powerful tool for both classification and regression as they are able to model many different phenomena and construct complex boundaries between classes. Nevertheless, the absence of efficient methods for obtaining manageable response surfaces for real-world problems due to the large number of terms needed, greatly undermines their applicability. In this paper we propose the use of real-coded genetic algorithms for overcoming these limitations. We apply the evolved response surfaces to classification in two classes. The proposed algorithm selects a model of minimum dimensionality improving the robustness and generalisation abilities of the obtained classifier. The algorithm uses a dual codification (real and binary) and specific operators adapted from the standard operators for real-coded algorithms. The fitness function considers the classification error and a regularisation term that takes into account the number of terms of the model. The results ob...

• Type: Conference
• Year: 2006
• Where: ESANN
• Authors: Rafael del Castillo Gomariz, Nicolás García-Pedrajas
• Keywords: ESANN 2006, ESANN 2007, Evolved Response Surfaces, Manageable Response Surfaces, Response Surfaces