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A Unified View of Multi-Label Performance Measures

arXiv 1609.00288 ↗First posted 1 Sep 2016cs.LGcited by 36

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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.

Key facts. Cited by 36 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Sep 2016.

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Citing papers per year: 2021: 10; 2022: 5; 2023: 6; 2024: 5; 2025: 7; 2026: 3.

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  3. 2021sigmoidF1: A Smooth F1 Score Surrogate Loss for Multilabel Classificationcited by 23
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  5. 2021Rethinking and Reweighting the Univariate Losses for Multi-Label Ranking: Consistency and Generalizationcited by 9
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  9. 2024Revisiting Hierarchical Text Classification: Inference and Metricscited by 7
  10. 2024Defining and Detecting Vulnerability in Human Evaluation Guidelines: A Preliminary Study Towards Reliable NLG Evaluationcited by 5
  11. 2025CrossHOI-Bench: A Unified Benchmark for HOI Evaluation across Vision-Language Models and HOI-Specific Methodscited by 5
  12. 2021Aerial Scene Understanding in The Wild: Multi-Scene Recognition via Prototype-based Memory Networkscited by 4
24 more
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  2. 2021Multi-label Ranking: Mining Multi-label and Label Ranking Datacited by 3
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  4. 2025Rethinking Consistent Multi-Label Classification Under Inexact Supervisioncited by 3
  5. 2021HMD-AMP: Protein Language-Powered Hierarchical Multi-label Deep Forest for Annotating Antimicrobial Peptidescited by 2
  6. 2023Label Attention Network for Temporal Sets Prediction: You Were Looking at a Wrong Self-Attentioncited by 2
  7. 2026Toward Enhancing Representation Learning in Federated Multi-Task Settingscited by 2
  8. 2021Where and When: Space-Time Attention for Audio-Visual Explanationscited by 1
  9. 2021On the Use of Unrealistic Predictions in Hundreds of Papers Evaluating Graph Representationscited by 1
  10. 2024MAC: A Benchmark for Multiple Attributes Compositional Zero-Shot Learningcited by 1
  11. 2024Top-K Pairwise Ranking: Bridging the Gap Among Ranking-Based Measures for Multi-Label Classificationcited by 1
  12. 2025Towards the Development of Balanced Synthetic Data for Correcting Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and Synthetic Data Generating Modelcited by 1
  13. 2025Interaction Concordance Index: Performance Evaluation for Interaction Prediction Methodscited by 1
  14. 2022WSLRec: Weakly Supervised Learning for Neural Sequential Recommendation Modelscited by 0
  15. 2022Estimating Multi-label Accuracy using Labelset Distributionscited by 0
  16. 2023Pushing One Pair of Labels Apart Each Time in Multi-Label Learning: From Single Positive to Full Labelscited by 0
  17. 2023Improved Naive Bayes with Mislabeled Datacited by 0
  18. 2023When Measures are Unreliable: Imperceptible Adversarial Perturbations toward Top-k Multi-Label Learningcited by 0
  19. 2024Implicit Regularization for Multi-label Feature Selectioncited by 0
  20. 2025MLMC: Interactive multi-label multi-classifier evaluation without confusion matricescited by 0
  21. 2025Improving Similar Case Retrieval Ranking Performance By Revisiting RankSVMcited by 0
  22. 2025A Systematic Literature Review on Multi-label Data Stream Classificationcited by 0
  23. 2026Position: Evaluation of ECG Representations Must Be Fixedcited by 0
  24. 2026Principled Algorithms for Optimizing Generalized Metrics in Multi-Label Learningcited by 0