Binary Rule Generation via Hamming Clustering
IEEE Transactions on Knowledge and Data Engineering
Rule extraction: using neural networks or for neural networks?
Journal of Computer Science and Technology
Influential Rule Search Scheme (IRSS)-A New Fuzzy Pattern Classifier
IEEE Transactions on Knowledge and Data Engineering
Extracting linguistic quantitative rules from supervised neural networks
International Journal of Knowledge-based and Intelligent Engineering Systems
A PSO-aided neuro-fuzzy classifier employing linguistic hedge concepts
Expert Systems with Applications: An International Journal
International Journal of Business Intelligence and Data Mining
Interpretable Piecewise Linear Classifier
Neural Information Processing
Knowledge based descriptive neural networks
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
A Note on a priori Estimations of Classification Circuit Complexity
Fundamenta Informaticae - Hardest Boolean Functions and O.B. Lupanov
A simple rule extraction method using a compact RBF neural network
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Detection of human faces in a compressed domain for video stratification
The Visual Computer: International Journal of Computer Graphics
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An effective algorithm for extracting M-of-N rules from trained feedforward neural networks is proposed. First, we train a network where each input of the data can only have one of the two possible values, -1 or one. Next, 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 simple and accurate