Generalized subsumption and its applications to induction and redundancy
Artificial Intelligence
The cascade-correlation learning architecture
Advances in neural information processing systems 2
Extracting Refined Rules from Knowledge-Based Neural Networks
Machine Learning
A penalty-function approach for pruning feedforward neural networks
Neural Computation
Extracting rules from neural networks by pruning and hidden-unit splitting
Neural Computation
Inductive Logic Programming: From Machine Learning to Software Engineering
Inductive Logic Programming: From Machine Learning to Software Engineering
Symbolic Interpretation of Artificial Neural Networks
IEEE Transactions on Knowledge and Data Engineering
Learning Logical Definitions from Relations
Machine Learning
Machine Learning
Extracting regression rules from neural networks
Neural Networks
Learning Nonrecursive Definitions of Relations with LINUS
EWSL '91 Proceedings of the European Working Session on Machine Learning
Second Order Derivatives for Network Pruning: Optimal Brain Surgeon
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Extracting rules for grammar recognition from Cascade-2 networks
Connectionist, Statistical, and Symbolic Approaches to Learning for Natural Language Processing
Neural network explanation using inversion
Neural Networks
Generating predicate rules from neural networks
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
IEEE Transactions on Neural Networks
Interpretation of artificial neural networks by means of fuzzy rules
IEEE Transactions on Neural Networks
Perceptron-based learning algorithms
IEEE Transactions on Neural Networks
An Efficient Explanation of Individual Classifications using Game Theory
The Journal of Machine Learning Research
Reverse Engineering the Neural Networks for Rule Extraction in Classification Problems
Neural Processing Letters
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Artificial neural networks (ANN) have demonstrated good predictive performance in a wide range of applications. They are, however, not considered sufficient for knowledge representation because of their inability to represent the reasoning process succinctly. This paper proposes a novel methodology Gyan that represents the knowledge of a trained network in the form of restricted first-order predicate rules. The empirical results demonstrate that an equivalent symbolic interpretation in the form of rules with predicates, terms and variables can be derived describing the overall behaviour of the trained ANN with improved comprehensibility while maintaining the accuracy and fidelity of the propositional rules.