# A Unified View of Multi-Label Performance Measures

arXiv: https://arxiv.org/abs/1609.00288 · HTML: https://mlsanity.com/paper/1609.00288/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2016.

## Built on by (most cited first, 30 of 36)

- [Open-environment Machine Learning](https://mlsanity.com/paper/2206.00423/) (2022)
- [Modeling Multi-Label Action Dependencies for Temporal Action Localization](https://mlsanity.com/paper/2103.03027/) (2021)
- [sigmoidF1: A Smooth F1 Score Surrogate Loss for Multilabel Classification](https://mlsanity.com/paper/2108.10566/) (2021)
- [MEDIC: A Multi-Task Learning Dataset for Disaster Image Classification](https://mlsanity.com/paper/2108.12828/) (2021)
- [Rethinking and Reweighting the Univariate Losses for Multi-Label Ranking: Consistency and Generalization](https://mlsanity.com/paper/2105.05026/) (2021)
- [Graph Neural Networks for Knowledge Enhanced Visual Representation of Paintings](https://mlsanity.com/paper/2105.08190/) (2021)
- [PulseImpute: A Novel Benchmark Task for Pulsative Physiological Signal Imputation](https://mlsanity.com/paper/2212.07514/) (2022)
- [SimFair: A Unified Framework for Fairness-Aware Multi-Label Classification](https://mlsanity.com/paper/2302.09683/) (2023)
- [Revisiting Hierarchical Text Classification: Inference and Metrics](https://mlsanity.com/paper/2410.01305/) (2024)
- [Defining and Detecting Vulnerability in Human Evaluation Guidelines: A Preliminary Study Towards Reliable NLG Evaluation](https://mlsanity.com/paper/2406.07935/) (2024)
- [CrossHOI-Bench: A Unified Benchmark for HOI Evaluation across Vision-Language Models and HOI-Specific Methods](https://mlsanity.com/paper/2508.18753/) (2025)
- [Aerial Scene Understanding in The Wild: Multi-Scene Recognition via Prototype-based Memory Networks](https://mlsanity.com/paper/2104.11200/) (2021)
- [Towards Understanding Generalization of Macro-AUC in Multi-label Learning](https://mlsanity.com/paper/2305.05248/) (2023)
- [Multi-label Ranking: Mining Multi-label and Label Ranking Data](https://mlsanity.com/paper/2101.00583/) (2021)
- [Confident Sinkhorn Allocation for Pseudo-Labeling](https://mlsanity.com/paper/2206.05880/) (2022)
- [Rethinking Consistent Multi-Label Classification Under Inexact Supervision](https://mlsanity.com/paper/2510.04091/) (2025)
- [HMD-AMP: Protein Language-Powered Hierarchical Multi-label Deep Forest for Annotating Antimicrobial Peptides](https://mlsanity.com/paper/2111.06023/) (2021)
- [Label Attention Network for Temporal Sets Prediction: You Were Looking at a Wrong Self-Attention](https://mlsanity.com/paper/2303.00280/) (2023)
- [Toward Enhancing Representation Learning in Federated Multi-Task Settings](https://mlsanity.com/paper/2602.01626/) (2026)
- [Where and When: Space-Time Attention for Audio-Visual Explanations](https://mlsanity.com/paper/2105.01517/) (2021)
- [On the Use of Unrealistic Predictions in Hundreds of Papers Evaluating Graph Representations](https://mlsanity.com/paper/2112.04274/) (2021)
- [MAC: A Benchmark for Multiple Attributes Compositional Zero-Shot Learning](https://mlsanity.com/paper/2406.12757/) (2024)
- [Top-K Pairwise Ranking: Bridging the Gap Among Ranking-Based Measures for Multi-Label Classification](https://mlsanity.com/paper/2407.06709/) (2024)
- [Towards the Development of Balanced Synthetic Data for Correcting Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and Synthetic Data Generating Model](https://mlsanity.com/paper/2502.05312/) (2025)
- [A Systematic Literature Review on Multi-label Data Stream Classification](https://mlsanity.com/paper/2508.17455/) (2025)
- [Interaction Concordance Index: Performance Evaluation for Interaction Prediction Methods](https://mlsanity.com/paper/2510.14419/) (2025)
- [WSLRec: Weakly Supervised Learning for Neural Sequential Recommendation Models](https://mlsanity.com/paper/2202.13616/) (2022)
- [Estimating Multi-label Accuracy using Labelset Distributions](https://mlsanity.com/paper/2209.04163/) (2022)
- [Pushing One Pair of Labels Apart Each Time in Multi-Label Learning: From Single Positive to Full Labels](https://mlsanity.com/paper/2302.14695/) (2023)
- [Improved Naive Bayes with Mislabeled Data](https://mlsanity.com/paper/2304.06292/) (2023)

## Abstract (arXiv)

Multi-label classification deals with the problem where each instance is associated with multiple class labels. Because evaluation in multi-label classification is more complicated than single-label setting, a number of performance measures have been proposed. It is noticed that an algorithm usually performs differently on different measures. Therefore, it is important to understand which algorithms perform well on which measure(s) and why. In this paper, we propose a unified margin view to revisit eleven performance measures in multi-label classification. In particular, we define label-wise margin and instance-wise margin, and prove that through maximizing these margins, different corresponding performance measures will be optimized. Based on the defined margins, a max-margin approach called LIMO is designed and empirical results verify our theoretical findings.