Communications of the ACM
Adaptive pattern recognition and neural networks
Adaptive pattern recognition and neural networks
C4.5: programs for machine learning
C4.5: programs for machine learning
Extracting Refined Rules from Knowledge-Based Neural Networks
Machine Learning
Automated knowledge acquisition
Automated knowledge acquisition
Effective Data Mining Using Neural Networks
IEEE Transactions on Knowledge and Data Engineering
Extracting Provably Correct Rules from Artificial Neural Networks
Extracting Provably Correct Rules from Artificial Neural Networks
Understanding neural networks via rule extraction
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Decision Support Systems - Special issue: Data mining for financial decision making
Discovering Trends in Large Datasets Using Neural Networks
Applied Intelligence
Extracting linguistic quantitative rules from supervised neural networks
International Journal of Knowledge-based and Intelligent Engineering Systems
Artificial Intelligence in Medicine
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Necessary first-person axioms of neuroconsciousness
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
Inversion of a neural network via interval arithmetic for rule extraction
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Method of knowledge representation on spatial classification
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
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
Reverse Engineering the Neural Networks for Rule Extraction in Classification Problems
Neural Processing Letters
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This paper suggests the 驴Input-Network-Training-Output-Extraction-Knowledge驴 framework to classify existing rule extraction algorithms for feedforward neural networks. Based on the suggested framework, we identify the major practices of existing algorithms as relying on the technique of generate and test, which leads to exponential complexity, relying on specialized network structure and training algorithms, which leads to limited applications and reliance on the interpretation of hidden nodes, which leads to proliferation of classification rules and their incomprehensibility. In order to generalize the applicability of rule extraction, we propose the rule extraction algorithm GeneraLized Analytic Rule Extraction (GLARE), and demonstrate its efficacy by comparing it with neural networks per se and the popular rule extraction program for decision trees, C4.5.