Interpretable Meta-weighting Sparse Neural Additive Networks for Datasets with Label Noise and Class Imbalance

Abstract

Black-box neural networks are inherently inscrutable, and their widespread use has triggered significant societal issues in crucial areas such as healthcare, finance and safety. In these high-stakes decision-making domains, the deployment of machine learning algorithms requires not only prediction accuracy but also their interpretability and robustness against data distribution shifts, such as outliers, label noise, and category imbalance. In this work, we propose a novel Meta-weighted Sparse Neural Additive Model (MSpNAM), which offers robustness through an efficient bilevel weighting policy and inherits strong explainability and representation capabilities from the additive modeling strategy. Furthermore, empirical results across multiple synthetic and real datasets, under various distribution shifts, demonstrate that MSpNAM can scale effectively and achieve superior performance in terms of robustness, interpretability, and anti-forgetting compared to some of the latest baselines.