Visual Sensitivity Analysis for Artificial Neural Networks

2006 ◽  
pp. 191-198 ◽  
Author(s):  
Roberto Therón ◽  
Juan Francisco De Paz
Keyword(s):  
Neural Networks ◽  
Sensitivity Analysis ◽  
Artificial Neural Networks ◽  
Visual Sensitivity ◽  
Artificial Neural

scholarly journals A State-Based Sensitivity Analysis for Distinguishing the Global Importance of Predictor Variables in Artificial Neural Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Ehsan Ardjmand ◽  
David F. Millie ◽  
Iman Ghalehkhondabi ◽  
William A. Young II ◽  
Gary R. Weckman
Keyword(s):  
Neural Networks ◽  
Sensitivity Analysis ◽  
Artificial Neural Networks ◽  
Network Models ◽  
Predictor Variables ◽  
Apparent Lack ◽  
Artificial Neural ◽  
High Degree ◽  
Universal Approximators ◽  
Empirical Approaches

Artificial neural networks (ANNs) are powerful empirical approaches used to model databases with a high degree of accuracy. Despite their recognition as universal approximators, many practitioners are skeptical about adopting their routine usage due to lack of model transparency. To improve the clarity of model prediction and correct the apparent lack of comprehension, researchers have utilized a variety of methodologies to extract the underlying variable relationships within ANNs, such as sensitivity analysis (SA). The theoretical basis of local SA (that predictors are independent and inputs other than variable of interest remain “fixed” at predefined values) is challenged in global SA, where, in addition to altering the attribute of interest, the remaining predictors are varied concurrently across their respective ranges. Here, a regression-based global methodology, state-based sensitivity analysis (SBSA), is proposed for measuring the importance of predictor variables upon a modeled response within ANNs. SBSA was applied to network models of a synthetic database having a defined structure and exhibiting multicollinearity. SBSA achieved the most accurate portrayal of predictor-response relationships (compared to local SA and Connected Weights Analysis), closely approximating the actual variability of the modeled system. From this, it is anticipated that skepticisms concerning the delineation of predictor influences and their uncertainty domains upon a modeled output within ANNs will be curtailed.

scholarly journals Sensitivity analysis of energy inputs in crop production using artificial neural networks

2018 ◽  
Vol 197 ◽  
pp. 992-998 ◽  
Author(s):  
Alireza Khoshroo ◽  
Ali Emrouznejad ◽  
Ahmadreza Ghaffarizadeh ◽  
Mehdi Kasraei ◽  
Mahmoud Omid
Keyword(s):  
Neural Networks ◽  
Sensitivity Analysis ◽  
Artificial Neural Networks ◽  
Crop Production ◽  
Energy Inputs ◽  
Artificial Neural

Entering the Black Box of Neural Networks

2003 ◽  
Vol 42 (03) ◽  
pp. 287-296 ◽  
Author(s):  
B. S. Gerber ◽  
T. G. Tape ◽  
R. S. Wigton ◽  
P. S. Heckerling
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Sensitivity Analysis ◽  
Logistic Regression ◽  
Artificial Neural Networks ◽  
Important Predictor ◽  
Predictor Variables ◽  
Linear Substitution ◽  
Relevance Analysis ◽  
Artificial Neural

Summary Objectives: Artificial neural networks have proved to be accurate predictive instruments in several medical domains, but have been criticized for failing to specify the information upon which their predictions are based. We used methods of relevance analysis and sensitivity analysis to determine the most important predictor variables for a validated neural network for community-acquired pneumonia. Methods: We studied a feed-forward, back-propagation neural network trained to predict pneumonia among patients presenting to an emergency department with fever or respiratory complaints. We used the methods of full retraining, weight elimination, constant substitution, linear substitution, and data permutation to identify a consensus set of important demographic, symptom, sign, and comorbidity predictors that influenced network output for pneumonia. We compared predictors identified by these methods to those identified by a weight propagation analysis based on the matrices of the network, and by logistic regression. Results: Predictors identified by these methods were clinically plausible, and were concordant with those identified by weight analysis, and by logistic regression using the same data. The methods were highly correlated in network error, and led to variable sets with errors below bootstrap 95% confidence intervals for networks with similar numbers of inputs. Scores for variable relevance tended to be higher with methods that precluded network retraining (weight elimination) or that permuted variable values (data permutation), compared with methods that permitted retraining (full retraining) or that approximated its effects (constant and linear substitution). Conclusion: Methods of relevance analysis and sensitivity analysis are useful for identifying important predictor variables used by artificial neural networks.

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