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 51–75 of 299 papers

TitleStatusHype
Multi-Label Noise Transition Matrix Estimation with Label Correlations: Theory and AlgorithmCode1
Multi-Label Knowledge DistillationCode1
Exploiting Multi-Label Correlation in Label Distribution Learning—0
When Measures are Unreliable: Imperceptible Adversarial Perturbations toward Top-k Multi-Label LearningCode0
Semantic-Aware Dual Contrastive Learning for Multi-label Image ClassificationCode1
Pseudo Labels for Single Positive Multi-Label Learning—0
Hierarchical Multi-Instance Multi-Label Learning for Detecting Propaganda Techniques—0
A Decentralized Spike-based Learning Framework for Sequential Capture in Discrete Perimeter Defense Problem—0
Understanding Label Bias in Single Positive Multi-Label Learning—0
Reviewing Evolution of Learning Functions and Semantic Information Measures for Understanding Deep Learning—0
Deep Partial Multi-Label Learning with Graph Disambiguation—0
Towards Understanding Generalization of Macro-AUC in Multi-label LearningCode0
Minimal Learning Machine for Multi-Label LearningCode0
Auxiliary Label Embedding for Multi-label Learning with Missing LabelsCode0
Class-Distribution-Aware Pseudo Labeling for Semi-Supervised Multi-Label LearningCode1
Graph based Label Enhancement for Multi-instance Multi-label learning—0
Light-weight Deep Extreme Multilabel ClassificationCode0
MER 2023: Multi-label Learning, Modality Robustness, and Semi-Supervised LearningCode2
Reliable Representations Learning for Incomplete Multi-View Partial Multi-Label Classification—0
Deep Double Incomplete Multi-view Multi-label Learning with Incomplete Labels and Missing ViewsCode0
DICNet: Deep Instance-Level Contrastive Network for Double Incomplete Multi-View Multi-Label ClassificationCode1
Incomplete Multi-View Multi-Label Learning via Label-Guided Masked View- and Category-Aware TransformersCode1
Pushing One Pair of Labels Apart Each Time in Multi-Label Learning: From Single Positive to Full Labels—0
Transductive Matrix Completion with Calibration for Multi-Task Learning—0
Multi-label learning with missing labels using sparse global structure for label-specific featuresCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SADCLCF179.8—Unverified