B. Mak et Rw. Blanning, AN EMPIRICAL MEASURE OF ELEMENT CONTRIBUTION IN NEURAL NETWORKS, Ieee transactions on systems man and cybernetics part C: applications and reviews, 28(4), 1998, pp. 561-564
A frequent complaint about neural net models is that they fail to expl
ain their results in any useful way, The problem is not a lack of info
rmation, but an abundance of information that is difficult to interpre
t. When trained, neural nets will provide a predicted output for a pos
ited input, and they can provide additional information in the form of
interelement connection strengths. But this latter information is of
little use to analysts and managers who wish to interpret the results
they have been given. In this paper, we develop a measure of the relat
ive importance of the various input elements and hidden layer elements
, and we use this to interpret the contribution of these components to
the outputs of the neural net.