Graph-Based Algorithms for Boolean Function Manipulation
IEEE Transactions on Computers
C4.5: programs for machine learning
C4.5: programs for machine learning
Bottom-up induction of oblivious read-once decision graphs: strengths and limitations
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Symbolic Representation of Neural Networks
Computer - Special issue: neural computing: companion issue to Spring 1996 IEEE Computational Science & Engineering
A search technique for rule extraction from trained neural networks
Non-Linear Analysis
Algorithms and Data Structures in VLSI Design
Algorithms and Data Structures in VLSI Design
Oblivious decision trees graphs and top down pruning
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
IEEE Transactions on Neural Networks
Recursive Neural Network Rule Extraction for Data With Mixed Attributes
IEEE Transactions on Neural Networks
Extracting Rules From Neural Networks as Decision Diagrams
IEEE Transactions on Neural Networks - Part 2
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Neural networks offer good generalization performance, noise robustness, and model complexity control. However, neural network mappings are expressed in terms of complicated mathematical functions that are inherently hard to understand. To overcome this limitation rule extraction methods have been proposed. This paper presents a novel method of rule extraction which recursively, in a top-down manner, builds a Reduced Ordered Decision Diagram. The diagram structure allows sharing of nodes, which partially overcomes two problems present in Decision Tree-based rule extraction - the problem of subtree replication and of training set fragmentation. A method for reducing the rule search space by identifying regions in which the network shows similar behavior is presented. Preliminary results of the method performance are reported.