Classifier Shift

Classifier shift, the discrepancy between training and testing data distributions affecting classifier performance, is a central challenge in machine learning. Current research focuses on mitigating this shift through techniques like modifying classifier outputs (e.g., in federated learning settings with imbalanced data) or designing classifiers that are less sensitive to distributional changes, including approaches using fixed classifiers and adversarial training within graph neural networks. Addressing classifier shift is crucial for building robust and reliable machine learning systems across diverse applications, improving generalization and reducing the impact of data heterogeneity.