论文标题
MLP中沙普利值的基线：从失踪到中立
A Baseline for Shapley Values in MLPs: from Missingness to Neutrality
论文作者
论文摘要
深层神经网络基于其准确性而获得了动力，但是它们的解释性经常受到批评。结果，它们被标记为黑匣子。作为回应，文献中已经提出了几种方法来解释其预测。在解释方法中，沙普利值是一种特征归因方法，它偏向于其强大的理论基础。但是，使用Shapley值对特征归因的分析需要选择代表缺失概念的基线。基线的任意选择可能会对该方法的解释力产生负面影响，并可能导致错误的解释。在本文中，我们提出了一种根据中立性值选择基线的方法：作为决策者选择的参数，其选择的点是由模型预测在其上方或低于其之上的参数。因此，提出的基线是基于取决于模型实际使用的参数设置的。此过程与设置其他基线的方式相反，即不考虑模型的使用方式。我们使用两个数据集在二进制分类任务的背景下验证了基线的选择：合成数据集和来自金融领域的数据集。
Deep neural networks have gained momentum based on their accuracy, but their interpretability is often criticised. As a result, they are labelled as black boxes. In response, several methods have been proposed in the literature to explain their predictions. Among the explanatory methods, Shapley values is a feature attribution method favoured for its robust theoretical foundation. However, the analysis of feature attributions using Shapley values requires choosing a baseline that represents the concept of missingness. An arbitrary choice of baseline could negatively impact the explanatory power of the method and possibly lead to incorrect interpretations. In this paper, we present a method for choosing a baseline according to a neutrality value: as a parameter selected by decision-makers, the point at which their choices are determined by the model predictions being either above or below it. Hence, the proposed baseline is set based on a parameter that depends on the actual use of the model. This procedure stands in contrast to how other baselines are set, i.e. without accounting for how the model is used. We empirically validate our choice of baseline in the context of binary classification tasks, using two datasets: a synthetic dataset and a dataset derived from the financial domain.
