Label Denoising and Counterfactual Explanation with A Plug and Play Framework
Wenting Qi, Charalampos Chelmis · 2022 IEEE International Conference on Big Data (Big Data) · 2022
Most supervised classification methods assume perfect training data, although this is not usually the case in the real–world. Meanwhile, counterfactual data generation approaches have emerged as a way to provide post–hoc explanation of decisions made by classification models. However, such approaches highly rely on the classification model output since different outputs lead to alternative, or even contradicting explanations. This work proposes a plug–and–play framework to learn a robust classification model in the presence of noisy labeled data and provide actionable suggestions for undesirable decisions (e.g., loan application rejection) made by a given classification model. The framework’s generalizability is demonstrated by considering alternative noisy label detection and counterfactual explanation methods, as well as diverse supervised classification models. The framework’s superiority against several baselines is demonstrated using three benchmark datasets.