Communications of the ACM
Integrating artificial neural networks with rule-based expert systems
Decision Support Systems - Special issue on neural networks for decision support
Information and Management
Decision Support Systems - Special issue: Data mining for financial decision making
A reactive approach to explanation
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
An empirical measure of element contribution in neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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Decision-making frequently involves identifying how to change input parameters in a given process in order to effect a directed change in the process output. Artificial neural networks have been used extensively to model business and manufacturing processes and there are several existing neural network-based influence measures that allow a decision-maker to assess the relative impact of each variable on process performance. The purpose of this paper is to review those neural network-based measures of variable influence, and to identify the combination of those measures that results in a comprehensive approach to characterizing variable influence within a trained neural network model. We then demonstrate how this comprehensive approach can be used as a tool to guide decision makers in dynamic process control.