ABSTRACT

Artificial neural networks evolve into deep learning recently and perform well in various fields, such as image and speech recognition and translation. However, there is a problem that it is difficult for a person to understand what exactly the trained knowledge of an artificial neural network. As one of the methods for solving the problem of the artificial neural network, rule extraction methods had been devised. In this study, rules are extracted from artificial neural networks using ordered-attribute search (OAS) algorithm, which is one of the methods of extracting rules from trained neural networks, and the rules are analyzed to improve the extracted rules. As a result, we found that when the output value of the hidden layer has an intermediate value that is not close to 0 or 1 after passing through the sigmoid function, the problem of rule uncertainty occurs and affects the accuracy of the rules. In order to solve the uncertainty problem of the rules, we applied the hidden unit clarification method and suggested that it is possible to extract the efficient rule by binarizing the hidden layer output value. In addition, we extracted CDRPs (critical data routing paths) from the trained neural networks and used CDRPs to prune the extracted rules, which showed that the uncertainty problem of rules can be improved.

摘要

人工神经网络最近发展成为深度学习，并且在图像和语音识别和翻译等各个领域都表现出色。然而，存在一个问题，即，一个人难以理解精确地训练过的人工神经网络知识。作为解决人工神经网络问题的方法之一，已经设计了规则提取方法。在这项研究中，使用有序属性搜索（OAS）算法从人工神经网络中提取规则，这是从训练后的神经网络中提取规则的方法之一，并对规则进行了分析以改进提取的规则。结果，我们发现，当通过S形函数后，隐藏层的输出值具有不接近0或1的中间值时，规则不确定性的问题会发生并影响规则的准确性。为了解决规则的不确定性问题，我们应用了隐藏单元澄清方法，并建议可以通过对隐藏层输出值进行二值化来提取有效规则。此外，我们从训练后的神经网络中提取了CDRP（关键数据路由路径），并使用CDRP修剪了提取的规则，这表明可以改善规则的不确定性问题。