ISSN:
1433-3058
Keywords:
Explanation capability
;
Interpretation
;
Knowledge discovery
;
Rule induction
;
Safety critical
;
Validation and verification
Source:
Springer Online Journal Archives 1860-2000
Topics:
Computer Science
,
Mathematics
Notes:
Abstract This paper interprets the outputs from a Multilayer Perceptron (MLP) network that performs a whole life assurance risk assessment task. Using a new method published by the first author, the paper finds the significant, or key, inputs that the network uses to classify applicants for whole life assurance into standard and non-standard risk. The ranking of the significant inputs enables the knowledge learned by the network during training to be presented in the form of data relationships and induced rules which show that the network learns sensibly and effectively when compared with the training data set. This study demonstrates the potential value of the knowledge discovery method for MLP network validation and case-by-case interpretation both during network learning and network use. This has important implications for safety critical systems.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1007/BF01501507
Permalink