Reduction of symbolic rules from artificial neural networks using sensitivity analysis
Reduction of symbolic rules from artificial neural networks using sensitivity analysis
复制标题
使用敏感性分析减少人工神经网络的符号规则
DOI：
10.1109/icnn.1995.488892
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发表时间：
1995
期刊：
影响因子：
--
通讯作者：
I. Cloete
中科院分区：
文献类型：
--
作者：
H. Viktor;A. Engelbrecht;I. Cloete
本文展示了如何灵敏度分析识别和消除冗余条件的规则提取训练的神经网络，通过消除不相关的输入。这导致规则的数量和大小的减少。减少规则集准确，最低限度地反映了分类问题。此外，冗余输入单元的消除显著减少了规则提取算法的组合。所得到的规则集与传统的符号机器学习算法相比毫不逊色。
This paper shows how sensitivity analysis identifies and eliminates redundant conditions from the rules extracted from trained neural networks, by eliminating irrelevant inputs. This leads to a reduction in the number and size of the rules. The reduced rule set accurately and minimally reflect the classification problems presented. Also, the elimination of redundant input units significantly reduces the combinatorics of the rule extraction algorithm. The resultant rule set compares favorably with traditional symbolic machine learning algorithms.