# Joint Ranking SVM and Binary Relevance with Robust Low-Rank Learning for Multi-Label Classification

arXiv: https://arxiv.org/abs/1911.01658 · HTML: https://mlsanity.com/paper/1911.01658/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2019.

## Built on by (most cited first, 10 of 10)

- [Exploiting Multi-Label Correlation in Label Distribution Learning](https://mlsanity.com/paper/2308.01742/) (2023)
- [Rethinking and Reweighting the Univariate Losses for Multi-Label Ranking: Consistency and Generalization](https://mlsanity.com/paper/2105.05026/) (2021)
- [Explainable machine learning multi-label classification of Spanish legal judgements](https://mlsanity.com/paper/2405.17610/) (2024)
- [Towards Understanding Generalization of Macro-AUC in Multi-label Learning](https://mlsanity.com/paper/2305.05248/) (2023)
- [Methods for Class-Imbalanced Learning with Support Vector Machines: A Review and an Empirical Evaluation](https://mlsanity.com/paper/2406.03398/) (2024)
- [Adaptive Multiscale Retinal Diagnosis: A Hybrid Trio-Model Approach for Comprehensive Fundus Multi-Disease Detection Leveraging Transfer Learning and Siamese Networks](https://mlsanity.com/paper/2405.18449/) (2024)
- [Classifier Chain Networks for Multi-Label Classification](https://mlsanity.com/paper/2411.02638/) (2024)
- [An Ensemble Learning Based Approach to Multi-label Power Text Classification for Fault-type Recognition](https://mlsanity.com/paper/2204.06179/) (2022)
- [Minimal Learning Machine for Multi-Label Learning](https://mlsanity.com/paper/2305.05518/) (2023)
- [Improving Similar Case Retrieval Ranking Performance By Revisiting RankSVM](https://mlsanity.com/paper/2502.11131/) (2025)

## Abstract (arXiv)

Multi-label classification studies the task where each example belongs to multiple labels simultaneously. As a representative method, Ranking Support Vector Machine (Rank-SVM) aims to minimize the Ranking Loss and can also mitigate the negative influence of the class-imbalance issue. However, due to its stacking-style way for thresholding, it may suffer error accumulation and thus reduces the final classification performance. Binary Relevance (BR) is another typical method, which aims to minimize the Hamming Loss and only needs one-step learning. Nevertheless, it might have the class-imbalance issue and does not take into account label correlations. To address the above issues, we propose a novel multi-label classification model, which joints Ranking support vector machine and Binary Relevance with robust Low-rank learning (RBRL). RBRL inherits the ranking loss minimization advantages of Rank-SVM, and thus overcomes the disadvantages of BR suffering the class-imbalance issue and ignoring the label correlations. Meanwhile, it utilizes the hamming loss minimization and one-step learning advantages of BR, and thus tackles the disadvantages of Rank-SVM including another thresholding learning step. Besides, a low-rank constraint is utilized to further exploit high-order label correlations under the assumption of low dimensional label space. Furthermore, to achieve nonlinear multi-label classifiers, we derive the kernelization RBRL. Two accelerated proximal gradient methods (APG) are used to solve the optimization problems efficiently. Extensive comparative experiments with several state-of-the-art methods illustrate a highly competitive or superior performance of our method RBRL.