Ordinal Classification

Ordinal classification tackles the problem of predicting ordered categorical variables, aiming to leverage the inherent relationships between classes beyond simple categorical distinctions. Current research emphasizes developing robust loss functions and model architectures, including deep learning approaches and transformer networks, that effectively capture ordinality, often incorporating techniques like distance regularization and weighted ranking similarity to improve performance, particularly for extreme classes. This field is significant for its applications in diverse areas such as medical image analysis, speech assessment, and natural language processing, where accurate prediction of ordered categories is crucial for improved diagnostics, personalized treatment, and enhanced understanding of complex phenomena.