Top-down induction of reduced ordered decision diagrams from neural networks
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
Review: Hybrid expert systems: A survey of current approaches and applications
Expert Systems with Applications: An International Journal
Reverse Engineering the Neural Networks for Rule Extraction in Classification Problems
Neural Processing Letters
A new neural data analysis approach using ensemble neural network rule extraction
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
Assessing scorecard performance: A literature review and classification
Expert Systems with Applications: An International Journal
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In this paper, we present a recursive algorithm for extracting classification rules from feedforward neural networks (NNs) that have been trained on data sets having both discrete and continuous attributes. The novelty of this algorithm lies in the conditions of the extracted rules: the rule conditions involving discrete attributes are disjoint from those involving continuous attributes. The algorithm starts by first generating rules with discrete attributes only to explain the classification process of the NN. If the accuracy of a rule with only discrete attributes is not satisfactory, the algorithm refines this rule by recursively generating more rules with discrete attributes not already present in the rule condition, or by generating a hyperplane involving only the continuous attributes. We show that for three real-life credit scoring data sets, the algorithm generates rules that are not only more accurate but also more comprehensible than those generated by other NN rule extraction methods.