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Abstracting Uncertain Knowledge: Case for Neural Nets Application


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Abstract

Neural networks have been used successfully in practical applications where human expertise exists but no clear rules are known. There is a more difficult case when the acquisition of labelled data points is very expensive, such as in labelling of ground data to match satellite images in geographic information systems.In fact, the dependence of neural networks on large volumes of training data result in the neural solution, producing more inconsistent results over a number of trials using the same data, but different initialisations of the weights.We present our method of generating IF-THEN rules expressing the trained neural networkaposs behaviour. By using the causal index on characteristic input patterns, we produce a list of inputs which were significant in reaching the decision made and a well-ordered sequence of rules governing this decision. This method correctly produced rules for 94percnt of the decisions made by a sample network.The principle of selecting the next most likely decision (that the network could have made) brings forth the question of specificity of the ensuing procedure. The extent to which each rule ldquooutweighsrdquo its successor implies the degree of our confidence in the correctness of the final decision. Viewing causal indices as degrees of possibility of rules relevance permits using the machinery of formal specificity measures to capture this notion quantitatively. We outline the methodology, which has a well-defined axiomatic basis, and leads to a parametrized family of specificity functions.


Keywords


Pages

Total Pages: 13
Pages: 365-377

DOI
10.1080/10798587.1995.10750642


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Published

Volume: 1
Issue: 4
Year: 1995

Cite this document


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JOURNAL INFORMATION


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)
Journal: 1995-Present




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