Advances in neural information processing systems 2
Symbolic Representation of Neural Networks
Computer - Special issue: neural computing: companion issue to Spring 1996 IEEE Computational Science & Engineering
Extracting rules from neural networks by pruning and hidden-unit splitting
Neural Computation
Data mining: concepts and techniques
Data mining: concepts and techniques
Introduction to Artificial Neural Systems
Introduction to Artificial Neural Systems
Reverse Engineering and Design Recovery: A Taxonomy
IEEE Software
Generalized Analytic Rule Extraction for Feedforward Neural Networks
IEEE Transactions on Knowledge and Data Engineering
Handling Continuous-Valued Attributes in Decision Tree with Neural Network Modelling
ECML '00 Proceedings of the 11th European Conference on Machine Learning
Generalization Performance of Overtrained Back-Propagation Networks
Proceedings of the EURASIP Workshop 1990 on Neural Networks
Extracting Provably Correct Rules from Artificial Neural Networks
Extracting Provably Correct Rules from Artificial Neural Networks
Discovering Trends in Large Datasets Using Neural Networks
Applied Intelligence
Rule Extraction from Recurrent Neural Networks: A Taxonomy and Review
Neural Computation
Neural network explanation using inversion
Neural Networks
Decision-tree instance-space decomposition with grouped gain-ratio
Information Sciences: an International Journal
Neural-Based Learning Classifier Systems
IEEE Transactions on Knowledge and Data Engineering
Rule extraction from trained adaptive neural networks using artificial immune systems
Expert Systems with Applications: An International Journal
Understanding neural networks via rule extraction
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Two-phase construction of multilayer perceptrons using information theory
IEEE Transactions on Neural Networks
An extension of the naive Bayesian classifier
Information Sciences: an International Journal
Neural network topology optimization
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
An iterative pruning algorithm for feedforward neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Extracting rules from trained neural networks
IEEE Transactions on Neural Networks
A new pruning heuristic based on variance analysis of sensitivity information
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Recursive Neural Network Rule Extraction for Data With Mixed Attributes
IEEE Transactions on Neural Networks
Comprehensible classification models: a position paper
ACM SIGKDD Explorations Newsletter
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Artificial neural networks often achieve high classification accuracy rates, but they are considered as black boxes due to their lack of explanation capability. This paper proposes the new rule extraction algorithm RxREN to overcome this drawback. In pedagogical approach the proposed algorithm extracts the rules from trained neural networks for datasets with mixed mode attributes. The algorithm relies on reverse engineering technique to prune the insignificant input neurons and to discover the technological principles of each significant input neuron of neural network in classification. The novelty of this algorithm lies in the simplicity of the extracted rules and conditions in rule are involving both discrete and continuous mode of attributes. Experimentation using six different real datasets namely iris, wbc, hepatitis, pid, ionosphere and creditg show that the proposed algorithm is quite efficient in extracting smallest set of rules with high classification accuracy than those generated by other neural network rule extraction methods.