# Multi-label Ranking: Mining Multi-label and Label Ranking Data

arXiv: https://arxiv.org/abs/2101.00583 · HTML: https://mlsanity.com/paper/2101.00583/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 169 references that are in the map.
- First posted on arXiv 3 Jan 2021.

## Builds on (most cited first, 30 of 33)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations](https://mlsanity.com/paper/1602.07332/) (2016)
- [ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases](https://mlsanity.com/paper/1705.02315/) (2017)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning](https://mlsanity.com/paper/1711.05225/) (2017)
- [Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks](https://mlsanity.com/paper/1503.00075/) (2015)
- [Zero-Shot Learning -- The Good, the Bad and the Ugly](https://mlsanity.com/paper/1703.04394/) (2017)
- [CNN-RNN: A Unified Framework for Multi-label Image Classification](https://mlsanity.com/paper/1604.04573/) (2016)
- [TieNet: Text-Image Embedding Network for Common Thorax Disease Classification and Reporting in Chest X-rays](https://mlsanity.com/paper/1801.04334/) (2018)
- [SGM: Sequence Generation Model for Multi-label Classification](https://mlsanity.com/paper/1806.04822/) (2018)
- [Learning Spatial Regularization with Image-level Supervisions for Multi-label Image Classification](https://mlsanity.com/paper/1702.05891/) (2017)
- [DiSMEC - Distributed Sparse Machines for Extreme Multi-label Classification](https://mlsanity.com/paper/1609.02521/) (2016)
- [CNN: Single-label to Multi-label](https://mlsanity.com/paper/1406.5726/) (2014)
- [Learning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated Image Annotation](https://mlsanity.com/paper/1603.08486/) (2016)
- [Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification](https://mlsanity.com/paper/1803.02315/) (2018)
- [Deep Convolutional Ranking for Multilabel Image Annotation](https://mlsanity.com/paper/1312.4894/) (2013)
- [Exploit Bounding Box Annotations for Multi-label Object Recognition](https://mlsanity.com/paper/1504.05843/) (2015)
- [A Unified View of Multi-Label Performance Measures](https://mlsanity.com/paper/1609.00288/) (2016)
- [Diagnose like a Radiologist: Attention Guided Convolutional Neural Network for Thorax Disease Classification](https://mlsanity.com/paper/1801.09927/) (2018)
- [Learning to recognize Abnormalities in Chest X-Rays with Location-Aware Dense Networks](https://mlsanity.com/paper/1803.04565/) (2018)
- [Deep Extreme Multi-label Learning](https://mlsanity.com/paper/1704.03718/) (2017)
- [Multi-Label Image Classification with Regional Latent Semantic Dependencies](https://mlsanity.com/paper/1612.01082/) (2016)
- [LSHTC: A Benchmark for Large-Scale Text Classification](https://mlsanity.com/paper/1503.08581/) (2015)
- [Boosted Cascaded Convnets for Multilabel Classification of Thoracic Diseases in Chest Radiographs](https://mlsanity.com/paper/1711.08760/) (2017)
- [A Structured Prediction Approach for Label Ranking](https://mlsanity.com/paper/1807.02374/) (2018)
- [Multi-Target Prediction: A Unifying View on Problems and Methods](https://mlsanity.com/paper/1809.02352/) (2018)
- [Fast Multi-Instance Multi-Label Learning](https://mlsanity.com/paper/1310.2049/) (2013)
- [Ensemble Methods for Multi-label Classification](https://mlsanity.com/paper/1307.1769/) (2013)
- [Dealing with Difficult Minority Labels in Imbalanced Mutilabel Data Sets](https://mlsanity.com/paper/1802.05033/) (2018)
- [Random Forest for Label Ranking](https://mlsanity.com/paper/1608.07710/) (2016)

## Built on by (most cited first, 3 of 3)

- [RLSEP: Learning Label Ranks for Multi-label Classification](https://mlsanity.com/paper/2212.04022/) (2022)
- [GaussianMLR: Learning Implicit Class Significance via Calibrated Multi-Label Ranking](https://mlsanity.com/paper/2303.03907/) (2023)
- [UniMLR: Modeling Implicit Class Significance for Multi-Label Ranking](https://mlsanity.com/paper/2508.21772/) (2025)

## Abstract (arXiv)

We survey multi-label ranking tasks, specifically multi-label classification and label ranking classification. We highlight the unique challenges, and re-categorize the methods, as they no longer fit into the traditional categories of transformation and adaptation. We survey developments in the last demi-decade, with a special focus on state-of-the-art methods in deep learning multi-label mining, extreme multi-label classification and label ranking. We conclude by offering a few future research directions.