A Survey on Ordinal Regression: Applications, Advances and Prospects

2025-03-02

AI Summary

This survey addresses the longstanding challenges in ordinal regression—namely, inadequate modeling of ordered categorical structures and a fragmented methodological landscape. To this end, we propose the first systematic three-tier taxonomy: (i) continuous-space discretization, (ii) distributional ordinal learning, and (iii) fuzzy instance mining. By establishing a unified conceptual framework, we provide the first paradigm-level categorization of mainstream approaches, formally delineating three principal technical pathways. We further conduct cross-domain empirical analysis—including facial age estimation, cancer staging, and image aesthetic assessment—to benchmark methodologies and elucidate their trade-offs. This work fills a critical gap in the field by delivering the first structured, comprehensive theoretical synthesis of ordinal regression. It offers foundational insights for algorithm design, interpretable modeling, and deployment in high-stakes domains such as clinical decision support and multimedia analytics.

Technology Category

• Machine Learning: Classification and Regression
• Knowledge Representation and Reasoning: Ontologies
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

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.

Problem

• Classify objects into ordered categories effectively.
• Enhance model interpretation using category order.
• Systematically categorize ordinal regression techniques and applications.

Innovation

• Systematic taxonomy for ordinal regression techniques
• Classification into Continuous Space Discretization
• Exploration of Distribution Ordering Learning methods