PARTIAL RETRAINING: A NEW APPROACH TO INPUT RELEVANCE DETERMINATION
Piërre van de Laar, Tom Heskes, Stan C. A. M. Gielen · International Journal of Neural Systems · 1999
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".