SOTAVerified

Multi-Label Learning

Multi-label learning (MLL) is a generalization of the binary and multi-category classification problems and deals with tagging a data instance with several possible class labels simultaneously [1]. Each of the assigned labels conveys a specific semantic relationship with the multi-label data instance [2, 3]. Multi-label learning has continued to receive a lot of research interest due to its practical application in many real-world problems such as recommender systems [4], image annotation [5], and text classification [6].

References:

  1. Kumar, S., Rastogi, R., Low rank label subspace transformation for multi-label learning with missing labels. Information Sciences 596, 53–72 (2022)

  2. Zhang M-L, Zhou Z-H (2013) A review on multi-label learning algorithms. IEEE Trans Knowl Data Eng 26(8):1819–1837

  3. Gibaja E, Ventura S (2015) A tutorial on multilabel learning. ACM Comput Surveys (CSUR) 47(3):1–38

  4. Bogaert M, Lootens J, Van den Poel D, Ballings M (2019) Evaluating multi-label classifiers and recommender systems in the financial service sector. Eur J Oper Res 279(2):620– 634

  5. Jing L, Shen C, Yang L, Yu J, Ng MK (2017) Multi-label classification by semi-supervised singular value decomposition. IEEE Trans Image Process 26(10):4612–4625

  6. Chen Z, Ren J (2021) Multi-label text classification with latent word-wise label information. Appl Intell 51(2):966–979

Papers

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Showing 201–225 of 299 papers

TitleStatusHype
Weakly Supervised Person Re-Identification—0
Component-Wise Boosting of Targets for Multi-Output Prediction—0
Semantic Bilinear Pooling for Fine-Grained Recognition—0
A Pseudo-Label Method for Coarse-to-Fine Multi-Label Learning with Limited Supervision—0
Collaboration based Multi-Label Learning—0
Pedestrian Attribute Recognition: A Survey—0
Multi-Label Adversarial Perturbations—0
Attention-Based Capsule Networks with Dynamic Routing for Relation Extraction—0
Group Preserving Label Embedding for Multi-Label Classification—0
Preference Based Adaptation for Learning Objectives—0
KDGAN: Knowledge Distillation with Generative Adversarial Networks—0
APLenty: annotation tool for creating high-quality datasets using active and proactive learning—0
An Interpretable Neural Network with Topical Information for Relevant Emotion Ranking—0
Few-Shot and Zero-Shot Multi-Label Learning for Structured Label SpacesCode0
Making Classifier Chains Resilient to Class Imbalance—0
CRAFTML, an Efficient Clustering-based Random Forest for Extreme Multi-label Learning—0
Learning a Compressed Sensing Measurement Matrix via Gradient UnrollingCode0
Incremental Sparse Bayesian Ordinal RegressionCode0
Relevant Emotion Ranking from Text Constrained with Emotion Relationships—0
Distribution-based Label Space Transformation for Multi-label Learning—0
Multi-label Learning Based Deep Transfer Neural Network for Facial Attribute Classification—0
Learning to Separate Object Sounds by Watching Unlabeled VideoCode0
Attentional Multilabel Learning over Graphs: A Message Passing Approach—0
Multi-label Learning with Missing Labels using Mixed Dependency Graphs—0
Tips, guidelines and tools for managing multi-label datasets: the mldr.datasets R package and the Cometa data repositoryCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SADCLCF179.8—Unverified