Classification by means of evolutionary response surfaces
Rafael del Castillo Gomariz, Nicolás García‐Pedrajas · The European Symposium on Artificial Neural Networks · 2006
Response surfaces are a powerful tool for both classifica- tion and regression as they are able to model many different phenomena and construct complex boundaries between classes. Nevertheless, the ab- sence of efficient methods for obtaining manageable responsesurfaces for real-world problems due to the large number of terms needed, greatly un- dermines their applicability. In this paper we propose the use of real-coded genetic algorithms for over- coming these limitations. We apply the evolved response surfaces to clas- sification in two classes. The proposed algorithm selects a model of mini- mum 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 er- ror and a regularisation term that takes into account the number of terms of the model. The results obtained in 10 real-world classification problems from the UCI Machine Learning Repository are comparable with well-known classifica- tion algorithms with a more interpretable polynomial function.