Title :
Feature selection for RBF networks
Author_Institution :
Inst. fur informatik, Univ. Frankfurt am Main, Frankfurt/Main, Germany
Abstract :
Radial basis function networks (RBFN) can be used for data classification. We present an a-posteriori feature selection method for trained RBFN that is not related to the specific learning procedure as long as every neuron belongs to exactly one class. Considering the center positions, the radii, the weights and the class labels of the neurons, we can easily calculate an index for feature selection that is based on one-dimensional projections. We present examples on different data sets by using Berthold and Diamond´s RBFN.
Keywords :
pattern classification; radial basis function networks; RBF networks; RBFN; a-posteriori feature selection method; center positions; class labels; data classification; data sets; feature selection; one-dimensional projections; radial basis function networks; trained RBFN; Emulation; Finite impulse response filter; Information filtering; Information filters; Network topology; Neural networks; Neurons; Radial basis function networks; Size control; Training data;
Conference_Titel :
Neural Information Processing, 2002. ICONIP '02. Proceedings of the 9th International Conference on
Print_ISBN :
981-04-7524-1
DOI :
10.1109/ICONIP.2002.1198208
