Ordinal Regression

Ordinal regression tackles the problem of classifying data into ordered categories, aiming to accurately reflect the inherent relationships between these categories and minimize prediction errors based on the distance from the true class. Current research focuses on improving model robustness to outliers and class imbalance, exploring various architectures including neural networks (e.g., support vector machines, conditional ordinal ranking networks) and adapting existing models like CLIP for improved performance. These advancements have significant implications across diverse fields, from medical image analysis and survival prediction to e-commerce and automatic pronunciation assessment, enabling more accurate and nuanced predictions in applications involving ordered data.