# An Effectiveness Metric for Ordinal Classification: Formal Properties and Experimental Results

arXiv: https://arxiv.org/abs/2006.01245 · HTML: https://mlsanity.com/paper/2006.01245/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2020.

## Built on by (most cited first, 7 of 7)

- [A Closer Look at Classification Evaluation Metrics and a Critical Reflection of Common Evaluation Practice](https://mlsanity.com/paper/2404.16958/) (2024)
- [AI-UPV at IberLEF-2021 DETOXIS task: Toxicity Detection in Immigration-Related Web News Comments Using Transformers and Statistical Models](https://mlsanity.com/paper/2111.04530/) (2021)
- [Enhancing Marker Scoring Accuracy through Ordinal Confidence Modelling in Educational Assessments](https://mlsanity.com/paper/2505.23315/) (2025)
- [A Dual-Perspective NLG Meta-Evaluation Framework with Automatic Benchmark and Better Interpretability](https://mlsanity.com/paper/2502.12052/) (2025)
- [Enhancing LLM-Based Data Annotation with Error Decomposition](https://mlsanity.com/paper/2601.11920/) (2026)
- [Contrastive Order Learning: A General Framework for Ordinal Regression](https://mlsanity.com/paper/2607.08109/) (2026)
- [PersonaEdit: Representative Sample Selection for Personalized Model Editing](https://mlsanity.com/paper/2608.27816/) (2026)

## Abstract (arXiv)

In Ordinal Classification tasks, items have to be assigned to classes that have a relative ordering, such as positive, neutral, negative in sentiment analysis. Remarkably, the most popular evaluation metrics for ordinal classification tasks either ignore relevant information (for instance, precision/recall on each of the classes ignores their relative ordering) or assume additional information (for instance, Mean Average Error assumes absolute distances between classes). In this paper we propose a new metric for Ordinal Classification, Closeness Evaluation Measure, that is rooted on Measurement Theory and Information Theory. Our theoretical analysis and experimental results over both synthetic data and data from NLP shared tasks indicate that the proposed metric captures quality aspects from different traditional tasks simultaneously. In addition, it generalizes some popular classification (nominal scale) and error minimization (interval scale) metrics, depending on the measurement scale in which it is instantiated.