C4.5: programs for machine learning
C4.5: programs for machine learning
Symbolic Representation of Neural Networks
Computer - Special issue: neural computing: companion issue to Spring 1996 IEEE Computational Science & Engineering
A penalty-function approach for pruning feedforward neural networks
Neural Computation
Rule extraction by successive regularization
Neural Networks
Extract intelligible and concise fuzzy rules from neural networks
Fuzzy Sets and Systems - Fuzzy systems
Machine Learning
Machine Learning
Chi2: Feature Selection and Discretization of Numeric Attributes
TAI '95 Proceedings of the Seventh International Conference on Tools with Artificial Intelligence
Extracting symbolic rules from trained neural network ensembles
AI Communications - Special issue on Artificial intelligence advances in China
A model for single and multiple knowledge based networks
Artificial Intelligence in Medicine
Neural-network feature selector
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Extracting 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
Computer Methods and Programs in Biomedicine
Application of rough sets theory in air quality assessment
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
Reverse Engineering the Neural Networks for Rule Extraction in Classification Problems
Neural Processing Letters
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This paper proposes a GRG (Greedy Rule Generation) algorithm, a new method for generating classification rules from a data set with discrete attributes. The algorithm is ''greedy'' in the sense that at every iteration, it searches for the best rule to generate. The criteria for the best rule include the number of samples and the size of subspaces that it covers, as well as the number of attributes in the rule. This method is employed for extracting rules from neural networks that have been trained and pruned for solving classification problems. The classification rules are extracted from the neural networks using the standard decompositional approach. Neural networks with one hidden layer are trained and the proposed GRG algorithm is applied to their discretized hidden unit activation values. Our experimental results show that neural network rule extraction with the GRG method produces rule sets that are accurate and concise. Application of GRG directly on three medical data sets with discrete attributes also demonstrates its effectiveness for rule generation.