ISSN:
1433-3058
Keywords:
Explanation facilities
;
Feedforward neural networks
;
Input impacts
;
Interpretation
;
Knowledge discovery
;
Weight analysis
Source:
Springer Online Journal Archives 1860-2000
Topics:
Computer Science
,
Mathematics
Notes:
Abstract Artificial neural network models are now being widely used in various areas of statistical research. Nevertheless, there is a certain degree of reluctance amongst members of the business profession in applying neural networks to business analysis. One of the major causes of scepticism is the inability of the models to provide explanation on how they reach their decisions. The current experiment is concerned with solving this problem by developing a framework for establishing the impacts of the input variables on the network output. The framework was tested on a feedforward neural network model for turnover forecasting which was developed in co-operation with a British retailer using real world marketing data. The results obtained are compared with those from a sensitivity analysis.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1007/BF01428122
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