论文标题
基于反事实分析的监督功能压缩
Supervised Feature Compression based on Counterfactual Analysis
论文作者
论文摘要
反事实解释已成为事后可解释的机器学习中的事实上的标准。对于给定的分类器和在不希望类中分类的实例，其反事实解释对应于该实例的小扰动，可以更改分类结果。这项工作旨在利用反事实解释来检测预先训练的黑盒模型的重要决策边界。此信息用于使用可调粒度来建立数据集中功能的监督离散化。使用离散的数据集，可以训练一个类似于黑框模型的最佳决策树，但这是可解释且紧凑的。实际数据集的数值结果在准确性和稀疏性方面显示了方法的有效性。
Counterfactual Explanations are becoming a de-facto standard in post-hoc interpretable machine learning. For a given classifier and an instance classified in an undesired class, its counterfactual explanation corresponds to small perturbations of that instance that allows changing the classification outcome. This work aims to leverage Counterfactual Explanations to detect the important decision boundaries of a pre-trained black-box model. This information is used to build a supervised discretization of the features in the dataset with a tunable granularity. Using the discretized dataset, an optimal Decision Tree can be trained that resembles the black-box model, but that is interpretable and compact. Numerical results on real-world datasets show the effectiveness of the approach in terms of accuracy and sparsity.
