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".
van de Laar
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Department of Medical Physics and Biophysics, University of Nijmegen, The Netherlands. pierre@mbfys.kun.nl
Heskes
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Gielen
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D000465:Algorithms; D003198:Computer Simulation; D016571:Neural Networks, Computer