A Survey on Ordinal Regression: Applications, Advances and Prospects
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arXiv
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| Format: | Preprint |
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2025
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| author | Wang, Jinhong Chen, Jintai Liu, Jian Tang, Dongqi Chen, Danny Z. Wu, Jian |
| author_facet | Wang, Jinhong Chen, Jintai Liu, Jian Tang, Dongqi Chen, Danny Z. Wu, Jian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_00952 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Survey on Ordinal Regression: Applications, Advances and Prospects Wang, Jinhong Chen, Jintai Liu, Jian Tang, Dongqi Chen, Danny Z. Wu, Jian Computer Vision and Pattern Recognition 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. |
| title | A Survey on Ordinal Regression: Applications, Advances and Prospects |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.00952 |