MLP中Shapley值的基线：从缺失到中性
A Baseline for Shapley Values in MLPs: from Missingness to Neutrality
摘要：
深度神经网络基于其准确性获得了发展势头，但其可解释性经常受到批评。因此，它们被标记为黑匣子。作为回应，文献中提出了几种方法来解释他们的预测。在解释方法中，Shapley值是一种特征归因方法，其理论基础坚实。然而，使用Shapley值分析特征属性需要选择代表缺失概念的基线。任意选择基线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，我们提出了一种方法来选择一个基线根据中立值：作为一个参数选择的决策者，在这一点上，他们的选择是由模型的预测是高于或低于它。因此，建议的基线是根据一个参数，这取决于模型的实际使用。这一程序与其他基线的设定方式形成对照，即不考虑如何使用模型。我们在二元分类任务的背景下，使用两个数据集：一个合成数据集和一个来自金融领域的数据集，实证验证了我们对基线的选择。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
