Setiono R
Department of Information Systems and Computer Science, National University of Singapore, Kent Ridge, Singapore.
Artif Intell Med. 1996 Feb;8(1):37-51. doi: 10.1016/0933-3657(95)00019-4.
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.
提出了一种用于神经网络剪枝的新算法。使用该算法，可获得连接数少且乳腺癌诊断准确率高的网络。然后，我们将描述如何通过仅考虑有限数量的隐藏单元激活值从剪枝后的网络中提取规则。提取规则的准确率与剪枝后网络的准确率一样高。对于乳腺癌诊断问题，从网络中提取的简洁规则在训练数据集和测试数据集上的准确率均超过95%。
