Reject Option

Reject option methods in machine learning enhance model reliability by allowing classifiers to abstain from predictions when confidence is low, improving accuracy and mitigating risks associated with incorrect classifications. Current research focuses on integrating reject options with various model architectures, including neural networks (e.g., using early exit strategies or variational autoencoders for out-of-distribution detection) and support vector classifiers, and developing explainable AI techniques to understand why a prediction was rejected. This research is significant because it addresses the limitations of traditional classifiers in handling uncertainty and improves the trustworthiness and applicability of machine learning models in high-stakes domains like healthcare and security.