Please use this identifier to cite or link to this item: https://doi.org/10.1016/0933-3657(95)00019-4
Title: Extracting rules from pruned neural networks for breast cancer diagnosis
Authors: Setiono, R. 
Keywords: Breast cancer diagnosis
Neural network pruning
Penalty function
Rule extraction
Issue Date: Feb-1996
Citation: Setiono, R. (1996-02). Extracting rules from pruned neural networks for breast cancer diagnosis. Artificial Intelligence in Medicine 8 (1) : 37-51. ScholarBank@NUS Repository. https://doi.org/10.1016/0933-3657(95)00019-4
Abstract: A new algorithm for neural network pruning is presented. Using this algorithm, networks with small number of connections and high accuracy rates for breast cancer diagnosis are obtained. We will then describe how rules can be extracted from a pruned network by considering only a finite number of hidden unit activation values. The accuracy of the extracted rules is as high as the accuracy of the pruned network. For the breast cancer diagnosis problem, the concise rules extracted from the network achieve an accuracy rate of more than 95% on the training data set and on the test data set.
Source Title: Artificial Intelligence in Medicine
URI: http://scholarbank.nus.edu.sg/handle/10635/99281
ISSN: 09333657
DOI: 10.1016/0933-3657(95)00019-4
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