Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Effective Data Mining Using Neural Networks
IEEE Transactions on Knowledge and Data Engineering
Generalized Analytic Rule Extraction for Feedforward Neural Networks
IEEE Transactions on Knowledge and Data Engineering
Data Mining with Computational Intelligence (Advanced Information and Knowledge Processing)
Data Mining with Computational Intelligence (Advanced Information and Knowledge Processing)
A GA-based RBF classifier with class-dependent features
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Rule extraction from an RBF classifier based on class-dependent features
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Extracting M-of-N rules from trained neural networks
IEEE Transactions on Neural Networks
Hi-index | 0.00 |
We propose a simple but efficient method to extract rules from the radial basis function (RBF) neural network. Firstly, the data are classified by an RBF classifier. During training the RBF network, we allow for large overlaps between clusters corresponding to the same class to reduce the number of hidden neurons while maintaining classification accuracy. Secondly, centers of the kernel functions are used as initial conditions when searching for rule premises by gradient descent. Thirdly, redundant rules and unimportant features are removed based on the rule tuning results. Simulations show that our approach results in accurate and concise rules.