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Abstract: Although backpropagation neural networks generally predict better than decision trees do for pattern classification problems, they are often regarded as black boxes, i.e., their predictions are not as interpretable as those of decision trees. This paper argues that this is because there has been no proper technique that enables us to do so. With an algorithm that can extract rules 1 , by drawing parallels with those of decision trees, we show that the predictions of a network can be ... (Update)
Context of citations to this paper: More
...to determine exactly how the neural network is making its decision. Techniques have been proposed to extract rules from neural networks [53], but these rules are not always accurate. Perceptrons do not suffer from this opaqueness; the perceptron s decision making process is...
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BibTeX entry: (Update)
Setiono, R. & Liu, H. (1995). Understanding neural networks via rule extraction. In Proc. of the 14th International Joint Conference on Artificial Intelligence, (pp. 480--485), Montreal, Canada. http://citeseer.ist.psu.edu/setiono95understanding.html More
@inproceedings{ setiono95understanding,
author = "Rudy Setiono and Huan Liu",
title = "Understanding Neural Networks via Rule Extraction",
booktitle = "{IJCAI}",
pages = "480-487",
year = "1995",
url = "citeseer.ist.psu.edu/setiono95understanding.html" }
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