Communications of the ACM
Extracting Refined Rules from Knowledge-Based Neural Networks
Machine Learning
Comparing connectionist and symbolic learning methods
Proceedings of a workshop on Computational learning theory and natural learning systems (vol. 1) : constraints and prospects: constraints and prospects
Fuzzy MLP based expert system for medical diagnosis
Fuzzy Sets and Systems - Special issue on fuzzy methods for computer vision and pattern recognition
Extracting rules from neural networks by pruning and hidden-unit splitting
Neural Computation
Rule-extraction by backpropagation of polyhedra
Neural Networks
Symbolic Interpretation of Artificial Neural Networks
IEEE Transactions on Knowledge and Data Engineering
Extracting regression rules from neural networks
Neural Networks
A Statistics Based Approach for Extracting Priority Rules from Trained Neural Networks
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3 - Volume 3
Rule Extraction from a Multi Layer Perceptron with Staircase Activation Functions
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3 - Volume 3
Extracting symbolic rules from trained neural network ensembles
AI Communications - Special issue on Artificial intelligence advances in China
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
On connectionism, rule extraction, and brain-like learning
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Neural Networks
Extracting M-of-N rules from trained neural networks
IEEE Transactions on Neural Networks
A new methodology of extraction, optimization and application of crisp and fuzzy logical rules
IEEE Transactions on Neural Networks
Interpretation of artificial neural networks by means of fuzzy rules
IEEE Transactions on Neural Networks
Extraction of rules from artificial neural networks for nonlinear regression
IEEE Transactions on Neural Networks
Rule-Based Learning Systems for Support Vector Machines
Neural Processing Letters
Machine learning: a review of classification and combining techniques
Artificial Intelligence Review
Knowledge discovery in corporate events by neural network rule extraction
Applied Intelligence
Supervised Machine Learning: A Review of Classification Techniques
Proceedings of the 2007 conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies
Rule extraction from support vector machines: A review
Neurocomputing
Generation of comprehensible hypotheses from gene expression data
BioDM'06 Proceedings of the 2006 international conference on Data Mining for Biomedical Applications
Oracle coached decision trees and lists
IDA'10 Proceedings of the 9th international conference on Advances in Intelligent Data Analysis
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In the research of rule extraction from neural networks, fidelity describes how well the rules mimic the behavior of a neural network while accuracy describes how well the rules can be generalized. This paper identifies the fidelity-accuracy dilemma. It argues to distinguish rule extraction using neural networks and rule extraction for neural networks according to their different goals, where fidelity and accuracy should be excluded from the rule quality evaluation framework, respectively.