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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 51–75 of 299 papers

TitleStatusHype
Learning by Minimizing the Sum of Ranked RangeCode0
Multi-label Iterated Learning for Image Classification with Label AmbiguityCode0
Multi-label learning for dynamic model type recommendationCode0
LIFT : Multi-Label Learning with Label-Specific FeaturesCode0
Incremental Sparse Bayesian Ordinal RegressionCode0
Label Ranker: Self-Aware Preference for Classification Label Position in Visual Masked Self-Supervised Pre-Trained ModelCode0
Multi-Moments in Time: Learning and Interpreting Models for Multi-Action Video UnderstandingCode0
Cut out and Replay: A Simple yet Versatile Strategy for Multi-Label Online Continual LearningCode0
IDEA: Increasing Text Diversity via Online Multi-Label Recognition for Vision-Language Pre-trainingCode0
Food Ingredients Recognition through Multi-label LearningCode0
Improving Predictions of Tail-end Labels using Concatenated BioMed-Transformers for Long Medical DocumentsCode0
Deep Double Incomplete Multi-view Multi-label Learning with Incomplete Labels and Missing ViewsCode0
Label Structure Preserving Contrastive Embedding for Multi-Label Learning with Missing LabelsCode0
Deep Region and Multi-Label Learning for Facial Action Unit DetectionCode0
DiSMEC - Distributed Sparse Machines for Extreme Multi-label ClassificationCode0
Discriminatory Label-specific Weights for Multi-label Learning with Missing LabelsCode0
Dual-Decoupling Learning and Metric-Adaptive Thresholding for Semi-Supervised Multi-Label LearningCode0
Atlas of Digital Pathology: A Generalized Hierarchical Histological Tissue Type-Annotated Database for Deep LearningCode0
Attack Transferability Characterization for Adversarially Robust Multi-label ClassificationCode0
Deep Streaming Label LearningCode0
Auxiliary Label Embedding for Multi-label Learning with Missing LabelsCode0
Variational Autoencoders for Sparse and Overdispersed Discrete DataCode0
A Consensual Collaborative Learning Method for Remote Sensing Image Classification Under Noisy Multi-LabelsCode0
Food Ingredients Recognition through Multi-label LearningCode0
Deep Extreme Multi-label LearningCode0
Show:102550
← PrevPage 3 of 12Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SADCLCF179.8—Unverified