A Survey on Ordinal Regression: Applications, Advances and Prospects
Journal:
arXiv
Published Date:
Mar 2, 2025
Abstract
Ordinal regression refers to classifying object instances into ordinal
categories. Ordinal regression is crucial for applications in various areas
like facial age estimation, image aesthetics assessment, and even cancer
staging, due to its capability to utilize ordered information effectively. More
importantly, it also enhances model interpretation by considering category
order, aiding the understanding of data trends and causal relationships.
Despite significant recent progress, challenges remain, and further
investigation of ordinal regression techniques and applications is essential to
guide future research. In this survey, we present a comprehensive examination
of advances and applications of ordinal regression. By introducing a systematic
taxonomy, we meticulously classify the pertinent techniques and applications
into three well-defined categories based on different strategies and
objectives: Continuous Space Discretization, Distribution Ordering Learning,
and Ambiguous Instance Delving. This categorization enables a structured
exploration of diverse insights in ordinal regression problems, providing a
framework for a more comprehensive understanding and evaluation of this field
and its related applications. To our best knowledge, this is the first
systematic survey of ordinal regression, which lays a foundation for future
research in this fundamental and generic domain.
