Supervised scaled regression clustering: an alternative to neural networks

Describes a method for the supervised training of regression systems that can be an alternative to feedforward artificial neural networks (ANNs) trained with the backpropagation algorithm. The proposed methodology is a hybrid structure based on supervised clustering with genetic algorithms and local learning. Supervised scaled regression clustering with genetic algorithms (SSRCGA) offers certain advantages related to robustness, generalization performance, feature selection, explanative behavior, and the additional flexibility of defining the fitness function and the regularization constraints. Computational results of SSRCGA are compared with backpropagation trained ANNs on a real-life environmental multivariate regression task.