First and second order sensitivity analysis of MLP

Neurocomputing

Volumn 73, Issue 10-12, 2010, Pages 2225-2233

  Yeh, I Cheng ^a   Cheng, Wei Lun ^a  

^a CHUNG HUA UNIVERSITY   (Taiwan)

Author keywords

Explanation ability; Multi layered perceptrons; Non linear model; Variable importance

Indexed keywords

BLACK BOXES; BLACK-BOX MODEL; FUNCTION MAPPING; IMPORTANCE INDEX; INPUT AND OUTPUTS; INPUT VARIABLES; MLP MODEL; MULTILAYERED PERCEPTRONS; NON-LINEAR MODEL; PREDICTION MODEL; REAL APPLICATIONS; SECOND ORDER EFFECT; SECOND-ORDER SENSITIVITY;

CYBERNETICS; MATHEMATICAL MODELS; PATTERN RECOGNITION SYSTEMS; SENSITIVITY ANALYSIS;

REAL VARIABLES;

ARTICLE; ARTIFICIAL NEURAL NETWORK; CLASSIFICATION ALGORITHM; COMPUTER MODEL; HUMAN COMPUTER INTERACTION; MATHEMATICAL COMPUTING; PERCEPTRON; PERFORMANCE MEASUREMENT SYSTEM; PRIORITY JOURNAL; SENSITIVITY ANALYSIS; STATISTICAL MODEL;

EID: 77952542781     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2010.01.011     Document Type: Article

References (14)

1. S. Hashem, Sensitivity analysis for feedforward artificial neural networks with differentiable activation functions, in: Proceedings of the International Joint Conference on Neural Networks, Baltimore, 1992, pp. 419-424.
2. J. Utans, J. Moody, S. Rehfuss, H. Siegelmann, Input variable selection for neural networks application to predicting the US business cycle, in: Proceedings of the IEEE/IAFE on Computational Intelligence for Financial Engineering, New York, 1995, pp. 118-122.
3. M.F. Redondo, C.H. Espinosa, Input selection by multilayer feedforward trained networks, in: Proceedings of the International Joint Conference on Neural Networks, Washington, 1999, pp. 1834-1839.
4. Guyon I., Elisseeff A. An introduction to variable and feature selection. The Journal of Machine Learning Research 2003, 3:1157-1182.
5. Hunter A. Application of neural networks and sensitivity analysis to improved prediction of trauma survival. Computer Methods and Programs in Biomedicine 2000, 62(1):11-19.
6. Engelbrecht A.P. A new pruning heuristic based on variance analysis of sensitivity information. IEEE Transactions on Neural Networks 2001, 12(6):1386-1399.
7. Yu L., Liu H. Efficient feature selection via analysis of relevance and redundancy. The Journal of Machine Learning Research 2004, 5:1205-1224.
8. Rakotomamonjy A. Variable selection using SVM based criteria. The Journal of Machine Learning Research 2003, 3:1357-1370.
9. Gevrey M., Dimopoulosb I., Leka S. Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecological Modelling 2003, 160(3):249-264.
10. Olden J.D., Joyb M.K., Deathb R.G. An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data. Ecological Modelling 2004, 178(3):389-397.
11. K.L. Joy, D. Primeaux, A comparison of two contributive analysis methods applied to an ANN modeling facial attractiveness, in: Fourth International Conference on Software Engineering Research, Management and Applications (SERA'06), 2006, pp. 82-86.
12. Myers R.H., Montgomery D.C. Response Surface Methodology 1995, Wiley, New York.
13. J.A. Blackard, Comparison of neural networks and discriminant analysis in predicting forest cover types, Ph.D. Dissertation, Department of Forest Sciences, Colorado State University, Fort Collins, Colorado, 1998.
14. UCI Machine Learning Repository Content Summary, Spambase Database, 2008. http://www.ics.uci.edu/~mlearn/MLSummary.html.