One v All Classifier

One-versus-all (OvA) classification is a widely used strategy for multi-class problems, where a separate binary classifier is trained for each class to distinguish it from all others. Current research focuses on improving OvA's performance within larger frameworks, such as extreme multi-label classification and open-set recognition, often integrating it with dual encoders, convolutional neural networks, or prototype-based methods. These advancements aim to enhance accuracy, calibration, and efficiency, particularly in handling large label spaces and ambiguous inputs. The resulting improvements have significant implications for various applications, including power grid classification, continual learning, and safer AI systems through calibrated learning-to-defer approaches.