论文标题
NN2RULES：从神经网络中提取规则列表
NN2Rules: Extracting Rule List from Neural Networks
论文作者
论文摘要
我们提出了一种算法NN2RULES，将训练有素的神经网络转换为规则列表。规则列表更容易解释，因为它们与人类做出决策的方式更好。 NN2RULES是一种规则提取的分解方法，即，它从训练有素的神经网络模型的参数中提取一组决策规则。我们表明，提取的决策规则在提出的任何输入上具有与神经网络相同的预测，因此具有相同的精度。 NN2RULES的一个关键贡献是，它允许隐藏的神经元行为是软二进制（例如Sigmoid激活）或整流的线性（relu），而不是使用软二进制激活而开发的现有分解方法。
We present an algorithm, NN2Rules, to convert a trained neural network into a rule list. Rule lists are more interpretable since they align better with the way humans make decisions. NN2Rules is a decompositional approach to rule extraction, i.e., it extracts a set of decision rules from the parameters of the trained neural network model. We show that the decision rules extracted have the same prediction as the neural network on any input presented to it, and hence the same accuracy. A key contribution of NN2Rules is that it allows hidden neuron behavior to be either soft-binary (eg. sigmoid activation) or rectified linear (ReLU) as opposed to existing decompositional approaches that were developed with the assumption of soft-binary activation.
