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 276–299 of 299 papers

TitleStatusHype
Deep Streaming Label LearningCode0
Neural Collapse in Multi-label Learning with Pick-all-label LossCode0
Sum of Ranked Range Loss for Supervised LearningCode0
Incremental Sparse Bayesian Ordinal RegressionCode0
A Simple but Effective Closed-form Solution for Extreme Multi-label LearningCode0
Semi-supervised multi-label feature selection via label correlation analysis with l1-norm graph embeddingCode0
Multi-Label Feature Selection Using Adaptive and Transformed RelevanceCode0
Towards Understanding Generalization of Macro-AUC in Multi-label LearningCode0
Multi-label Iterated Learning for Image Classification with Label AmbiguityCode0
Cost-Sensitive Reference Pair Encoding for Multi-Label LearningCode0
Object-Aware Self-supervised Multi-Label LearningCode0
Multi-label learning for dynamic model type recommendationCode0
Label Ranker: Self-Aware Preference for Classification Label Position in Visual Masked Self-Supervised Pre-Trained ModelCode0
Label Structure Preserving Contrastive Embedding for Multi-Label Learning with Missing LabelsCode0
Atlas of Digital Pathology: A Generalized Hierarchical Histological Tissue Type-Annotated Database for Deep LearningCode0
Boosting Single Positive Multi-label Classification with Generalized Robust LossCode0
Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label DistributionCode0
Incomplete Multi-View Weak-Label Learning with Noisy Features and Imbalanced LabelsCode0
A hybrid algorithm for Bayesian network structure learning with application to multi-label learningCode0
A Consensual Collaborative Learning Method for Remote Sensing Image Classification Under Noisy Multi-LabelsCode0
Learning a Compressed Sensing Measurement Matrix via Gradient UnrollingCode0
Learning by Minimizing the Sum of Ranked RangeCode0
When Measures are Unreliable: Imperceptible Adversarial Perturbations toward Top-k Multi-Label LearningCode0
Simplified and Unified Analysis of Various Learning Problems by Reduction to Multiple-Instance LearningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SADCLCF179.8—Unverified