一种基于功能性观点的神经网络规则提取方法

Method for Rule Extraction from Neural Networks Based on Functional Point of View

研究一种基于功能性观点的神经网络规则提取方法.阐述特征排序与选择、连续属性离散化、训练样本产生、神经网络训练、示例样本产生及规则提取等关键算法.并用 UCI 数据和人群分类数据对方法进行分析和验证.结果表明本文方法的正确有效性.

A method for rule extraction from neural networks based on the functional point of view is studied. The key algorithms are introduced, including the sort and selection of features, the discretization of continuous attributes, the generation of training samples of neural network （ NN）, the training of NN, the generation of the instance samples from the trained NN, and the rule extraction. The UCI data and the population classifying data are used to verify the rule extraction method. The results show the correction and effectivity of the proposed method.