序数回归综述：应用、进展与展望
A Survey on Ordinal Regression: Applications, Advances and Prospects
AI总结：
本综述系统回顾了序数回归的进展与应用，提出基于连续空间离散化、分布排序学习和模糊实例挖掘的分类法，为该基础领域的未来研究奠定框架。
摘要：
序数回归是指将对象实例分类到有序类别中。由于能够有效利用有序信息，序数回归在面部年龄估计、图像美学评估甚至癌症分期等各个领域的应用中至关重要。更重要的是，它还通过考虑类别顺序来增强模型可解释性，有助于理解数据趋势和因果关系。尽管近期取得了显著进展，但挑战依然存在，对序数回归技术和应用的进一步研究对于指导未来研究至关重要。在本综述中，我们对序数回归的进展和应用进行了全面考察。通过引入系统分类法，我们根据不同策略和目标将相关技术和应用细致地分为三个明确定义的类别：连续空间离散化、分布排序学习和模糊实例挖掘。这种分类方法使得对序数回归问题中多样化见解的结构化探索成为可能，为更全面地理解和评估该领域及其相关应用提供了一个框架。据我们所知，这是首个关于序数回归的系统综述，为这一基础和通用领域的未来研究奠定了基础。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
