# Comprehensive Comparative Study of Multi-Label Classification Methods

arXiv: https://arxiv.org/abs/2102.07113 · HTML: https://mlsanity.com/paper/2102.07113/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 105 references that are in the map.
- First posted on arXiv 14 Feb 2021.

## Builds on (most cited first, 6 of 6)

- [The Emerging Trends of Multi-Label Learning](https://mlsanity.com/paper/2011.11197/) (2020)
- [A scikit-based Python environment for performing multi-label classification](https://mlsanity.com/paper/1702.01460/) (2017)
- [Ensemble Methods for Multi-label Classification](https://mlsanity.com/paper/1307.1769/) (2013)
- [Deep Learning for Multi-label Classification](https://mlsanity.com/paper/1502.05988/) (2014)
- [Cost-Sensitive Label Embedding for Multi-Label Classification](https://mlsanity.com/paper/1603.09048/) (2016)
- [Multi-label classification search space in the MEKA software](https://mlsanity.com/paper/1811.11353/) (2018)

## Built on by (most cited first, 30 of 38)

- [A Comprehensive Survey on Applications of Transformers for Deep Learning Tasks](https://mlsanity.com/paper/2306.07303/) (2023)
- [An Exploration of Encoder-Decoder Approaches to Multi-Label Classification for Legal and Biomedical Text](https://mlsanity.com/paper/2305.05627/) (2023)
- [Multi-Label Learning with Stronger Consistency Guarantees](https://mlsanity.com/paper/2407.13746/) (2024)
- [Deep Learning for Multi-Label Learning: A Comprehensive Survey](https://mlsanity.com/paper/2401.16549/) (2024)
- [ZLPR: A Novel Loss for Multi-label Classification](https://mlsanity.com/paper/2208.02955/) (2022)
- [Correlation-Aware Graph Convolutional Networks for Multi-Label Node Classification](https://mlsanity.com/paper/2411.17350/) (2024)
- [CT-AGRG: Automated Abnormality-Guided Report Generation from 3D Chest CT Volumes](https://mlsanity.com/paper/2408.11965/) (2024)
- [SATA-BENCH: Select All That Apply Benchmark for Multiple Choice Questions](https://mlsanity.com/paper/2506.00643/) (2025)
- ["Is a picture of a bird a bird": Policy recommendations for dealing with ambiguity in machine vision models](https://mlsanity.com/paper/2306.15777/) (2023)
- [Characterizing Information Seeking Events in Health-Related Social Discourse](https://mlsanity.com/paper/2308.09156/) (2023)
- [Probably Approximately Precision and Recall Learning](https://mlsanity.com/paper/2411.13029/) (2024)
- [From Multi-label Learning to Cross-Domain Transfer: A Model-Agnostic Approach](https://mlsanity.com/paper/2207.11742/) (2022)
- [Hoeffding adaptive trees for multi-label classification on data streams](https://mlsanity.com/paper/2410.20242/) (2024)
- [Automated Multi-Label Annotation for Mental Health Illnesses Using Large Language Models](https://mlsanity.com/paper/2412.03796/) (2024)
- [Tree-Based Dynamic Classifier Chains](https://mlsanity.com/paper/2112.06672/) (2021)
- [SurvBench: A Standardised Preprocessing Pipeline for Multi-Modal Electronic Health Record Survival Analysis](https://mlsanity.com/paper/2511.11935/) (2025)
- [Classifier Chain Networks for Multi-Label Classification](https://mlsanity.com/paper/2411.02638/) (2024)
- [Correcting Noisy Multilabel Predictions: Modeling Label Noise through Latent Space Shifts](https://mlsanity.com/paper/2502.14281/) (2025)
- [Online Set Learning from Precision and Recall Feedback](https://mlsanity.com/paper/2605.09565/) (2026)
- [Explainable Model-specific Algorithm Selection for Multi-Label Classification](https://mlsanity.com/paper/2211.11227/) (2022)
- [FAIRification of MLC data](https://mlsanity.com/paper/2211.12757/) (2022)
- [Harnessing label semantics to extract higher performance under noisy label for Company to Industry matching](https://mlsanity.com/paper/2212.01685/) (2022)
- [Minimal Learning Machine for Multi-Label Learning](https://mlsanity.com/paper/2305.05518/) (2023)
- [Imbalanced Multi-label Classification for Business-related Text with Moderately Large Label Spaces](https://mlsanity.com/paper/2306.07046/) (2023)
- [Cost-constrained multi-label group feature selection using shadow features](https://mlsanity.com/paper/2408.01851/) (2024)
- [Label Cluster Chains for Multi-Label Classification](https://mlsanity.com/paper/2411.00514/) (2024)
- [A Trustworthy By Design Classification Model for Building Energy Retrofit Decision Support](https://mlsanity.com/paper/2504.06055/) (2025)
- [Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data](https://mlsanity.com/paper/2506.05721/) (2025)
- [Multi-Label Classification with Generative AI Models in Healthcare: A Case Study of Suicidality and Risk Factors](https://mlsanity.com/paper/2507.17009/) (2025)
- [Estimating Conditional Covariance between labels for Multilabel Data](https://mlsanity.com/paper/2508.18951/) (2025)

## Abstract (arXiv)

Multi-label classification (MLC) has recently received increasing interest from the machine learning community. Several studies provide reviews of methods and datasets for MLC and a few provide empirical comparisons of MLC methods. However, they are limited in the number of methods and datasets considered. This work provides a comprehensive empirical study of a wide range of MLC methods on a plethora of datasets from various domains. More specifically, our study evaluates 26 methods on 42 benchmark datasets using 20 evaluation measures. The adopted evaluation methodology adheres to the highest literature standards for designing and executing large scale, time-budgeted experimental studies. First, the methods are selected based on their usage by the community, assuring representation of methods across the MLC taxonomy of methods and different base learners. Second, the datasets cover a wide range of complexity and domains of application. The selected evaluation measures assess the predictive performance and the efficiency of the methods. The results of the analysis identify RFPCT, RFDTBR, ECCJ48, EBRJ48 and AdaBoostMH as best performing methods across the spectrum of performance measures. Whenever a new method is introduced, it should be compared to different subsets of MLC methods, determined on the basis of the different evaluation criteria.