Abstract
In this paper we propose an approach to variable selection that uses a neural-network model as the tool to determine which variables are to be discarded. The method performs a backward selection by successively removing input nodes in a network trained with the complete set of variables as inputs. Input nodes are removed, along with their connections, and remaining weights are adjusted in such a way that the overall input–output behavior learnt by the network is kept approximately unchanged. A simple criterion to select input nodes to be removed is developed. The proposed method is tested on a famous example of system identification. Experimental results show that the removal of input nodes from the neural network model improves its generalization ability. In addition, the method compares favorably with respect to other feature reduction methods.
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