This paper presents two approaches to extracting rules from a trained neural network consisting of linear threshold functions. The first one leads to an algorithm that extracts rules in the form of Boolean functions. Compared with an existing one, this algorithm outputs much more concise rules if the threshold functions correspond to 1-decision lists, majority functions, or certain combinations of these. The second one extracts probabilistic rules representing relations between some of the input variables and the output using a dynamic programming algorithm. The algorithm runs in pseudo-polynomial time if each hidden layer has a constant number of neurons. We demonstrate the effectiveness of these two approaches by computational experiments.

中文翻译：

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从训练有素的神经网络中提取布尔规则和概率规则。

本文提出了两种从线性阈值函数组成的训练神经网络中提取规则的方法。第一个导致了一种算法，该算法以布尔函数的形式提取规则。与现有算法相比，如果阈值函数对应于1决策列表，多数函数或它们的某些组合，则该算法输出的规则要简洁得多。第二种方法使用动态编程算法提取表示某些输入变量与输出之间关系的概率规则。如果每个隐藏层具有恒定数量的神经元，则该算法将在伪多项式时间内运行。我们通过计算实验证明了这两种方法的有效性。