Partial retraining: a new approach to input relevance determination.
In this article we introduce partial retraining, an algorithm to determine the relevance of the input variables of a trained neural network. We place this algorithm in the context of other approaches to relevance determination. Numerical experiments on both artificial and real-world problems show that partial retraining outperforms its competitors, which include methods based on constant substitution, analysis of weight magnitudes, and "optimal brain surgeon".
Department of Medical Physics and Biophysics, University of Nijmegen, The Netherlands. pierre@mbfys.kun.nl
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D000465:Algorithms; D003198:Computer Simulation; D016571:Neural Networks, Computer