Rule extraction from trained neural networks using genetic algorithms
Proceedings of the second world congress on Nonlinear analysts: part 3
Template-based procedures for neural network interpretation
Neural Networks
A search technique for rule extraction from trained neural networks
Non-Linear Analysis
Interpretation of Trained Neural Networks by Rule Extraction
Proceedings of the International Conference, 7th Fuzzy Days on Computational Intelligence, Theory and Applications
Extracting Provably Correct Rules from Artificial Neural Networks
Extracting Provably Correct Rules from Artificial Neural Networks
Extraction of rules from artificial neural networks for nonlinear regression
IEEE Transactions on Neural Networks
Genetic rule extraction optimizing brier score
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Expert Systems with Applications: An International Journal
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GEX (Genetic Rule EXtraction) method described in this paper is a method of rule extraction from a trained neural network. The acquired set of rules describes the performance of a neural network solving classification problems. The extracted rules are in the form of IF - THEN. The premises standing after IF set some constrains on the neural network input values. After THEN stands a designated class. The method is based on a genetic approach. GEX consists of subpopulations evolving on islands. The number of classes existing in the classification problem solved by the neural network assigns the number of subpopulations. The details of the method are presented in the paper. The results of experiments performed on well-known benchmark data sets are shown as well.