Abstracting Uncertain Knowledge: Case for Neural Nets Application

Abstract

ABSTRACTNeural 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 network's 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 94% of the decisions made by a sample network.The principle of selecting the next most likely decision (that the ...