MICAI '02 Proceedings of the Second Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
Extracting symbolic rules from trained neural network ensembles
AI Communications - Special issue on Artificial intelligence advances in China
Extracting symbolic rules from trained neural network ensembles
AI Communications - Artificial Intelligence Advances in China
Letters: Support vector machine interpretation
Neurocomputing
Knowledge discovery in corporate events by neural network rule extraction
Applied Intelligence
A neural tree and its application to spam e-mail detection
Expert Systems with Applications: An International Journal
Environmental Modelling & Software
An investigation of TREPAN utilising a continuous oracle model
International Journal of Data Analysis Techniques and Strategies
Support vector machine tree based on feature selection
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
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Although artificial neural networks can represent a variety of complex systems with a high degree of accuracy, these connectionist models are difficult to interpret. This significantly limits the applicability of neural networks in practice, especially where a premium is placed on the comprehensibility or reliability of systems. A novel artificial neural-network decision tree algorithm (ANN-DT) is therefore proposed, which extracts binary decision trees from a trained neural network. The ANN-DT algorithm uses the neural network to generate outputs for samples interpolated from the training data set. In contrast to existing techniques, ANN-DT can extract rules from feedforward neural networks with continuous outputs. These rules are extracted from the neural network without making assumptions about the internal structure of the neural network or the features of the data. A novel attribute selection criterion based on a significance analysis of the variables on the neural-network output is examined. It is shown to have significant benefits in certain cases when compared with the standard criteria of minimum weighted variance over the branches. In three case studies the ANN-DT algorithm compared favorably with CART, a standard decision tree algorithm