Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Comparison of neural networks and discriminant analysis in predicting forest cover types
Comparison of neural networks and discriminant analysis in predicting forest cover types
An introduction to variable and feature selection
The Journal of Machine Learning Research
Variable selection using svm based criteria
The Journal of Machine Learning Research
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
A Comparison of Two Contributive Analysis Methods Applied to an ANN Modeling Facial Attractiveness
SERA '06 Proceedings of the Fourth International Conference on Software Engineering Research, Management and Applications
A new pruning heuristic based on variance analysis of sensitivity information
IEEE Transactions on Neural Networks
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Multi-layered perceptrons (MLP) can build accurate classification and function mapping models; however, they have also been labeled a ''black box'' because they provide little explanatory insight into the contributions of the input variables in the prediction model. In this study, we derived the first- and second-order effect index and importance index through the differential statistical method. To verify these indexes, this study employed two man-made examples and two real application examples to test the performance of these indexes. The results showed that these indexes can really identify important variables and discover the relations between input and output variables; therefore, they give MLP some explanation ability, and raise the transparency of MLP model to overcome its black-box model shortcomings.