A search technique for rule extraction from trained neural networks
Search methods for rule extraction from neural networks work by finding those combinations of inputs that make the neuron active. By sorting the input weights to a neuron and ordering the weights suitably, it is possible to prune the search space. Based on this observation, we present an algorithm for rule extraction from feedforward neural networks with boolean inputs and analyze its properties.
rule extraction neural networks knowledge discovery