Extracting M-of-N rules from trained neural networks.

An effective algorithm for extracting M-of-N rules from trained feedforward neural networks is proposed. Two components of the algorithm distinguish our method from previously proposed algorithms which extract symbolic rules from neural networks. First, we train a network where each input of the data can only have one of the two possible values, -1 or one. Second, we apply the hyperbolic tangent function to each connection from the input layer to the hidden layer of the network. By applying this squashing function, the activation values at the hidden units are effectively computed as the hyperbolic tangent (or the sigmoid) of the weighted inputs, where the weights have magnitudes that are equal one. By restricting the inputs and the weights to binary values either -1 or one, the extraction of M-of-N rules from the networks becomes trivial. We demonstrate the effectiveness of the proposed algorithm on several widely tested datasets. For datasets consisting of thousands of patterns with many attributes, the rules extracted by the algorithm are surprisingly simple and accurate.