论文标题
基于副群体的可视化技术，用于神经网络
A copula-based visualization technique for a neural network
论文作者
论文摘要
机器学习的可解释性定义为人类可以理解决策的原因的程度。但是，由于其决策过程中的歧义，神经网络不可解释。因此，在这项研究中，我们提出了一种新的算法，该算法揭示了训练有素的神经网络的特征是重要的，并且在决策过程中主要追溯到哪些路径。在拟议的算法中，定义了通过应用配对概念概念来计算的神经网络层之间的相关系数估计的分数。我们将估计的分数与随机森林的特征重要性值进行了比较，这有时在实验中被视为高度可解释的算法，并确认结果彼此一致。该算法提出了一种压缩神经网络及其参数调整的方法，因为该算法识别有助于分类或预测结果的路径。
Interpretability of machine learning is defined as the extent to which humans can comprehend the reason of a decision. However, a neural network is not considered interpretable due to the ambiguity in its decision-making process. Therefore, in this study, we propose a new algorithm that reveals which feature values the trained neural network considers important and which paths are mainly traced in the process of decision-making. In the proposed algorithm, the score estimated by the correlation coefficients between the neural network layers that can be calculated by applying the concept of a pair copula was defined. We compared the estimated score with the feature importance values of Random Forest, which is sometimes regarded as a highly interpretable algorithm, in the experiment and confirmed that the results were consistent with each other. This algorithm suggests an approach for compressing a neural network and its parameter tuning because the algorithm identifies the paths that contribute to the classification or prediction results.
