Selection of important input variables for RBF network using partial derivatives

In regression problems, making accurate predictions is often the primary goal. Also, relevance of inputs in the prediction of an output would be valuable information in many cases. A sequential input selection algorithm for Radial basis function (SISAL-RBF) networks is presented to analyze importances of the inputs. The ranking of inputs is based on values, which are evaluated from the partial derivatives of the network. The proposed method is applied to benchmark data sets. It yields accurate prediction models, which are parsimonious in terms of the input variables.

• Type: Conference
• Year: 2008
• Where: ESANN
• Authors: Jarkko Tikka, Jaakko Hollmén
• Keywords: Accurate Prediction, ESANN 2008, Neural Networks, Radial Basis Function, Sequential Input Selection