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 76–100 of 299 papers

TitleStatusHype
LIFT : Multi-Label Learning with Label-Specific FeaturesCode0
Discovering Multi-Label Actor-Action Association in a Weakly Supervised SettingCode0
Discriminatory Label-specific Weights for Multi-label Learning with Missing LabelsCode0
Incremental Sparse Bayesian Ordinal RegressionCode0
Label Ranker: Self-Aware Preference for Classification Label Position in Visual Masked Self-Supervised Pre-Trained ModelCode0
Boosting Single Positive Multi-label Classification with Generalized Robust LossCode0
Dual-Decoupling Learning and Metric-Adaptive Thresholding for Semi-Supervised Multi-Label LearningCode0
Boost-RS: Boosted Embeddings for Recommender Systems and its Application to Enzyme-Substrate Interaction PredictionCode0
Atlas of Digital Pathology: A Generalized Hierarchical Histological Tissue Type-Annotated Database for Deep LearningCode0
Attack Transferability Characterization for Adversarially Robust Multi-label ClassificationCode0
Food Ingredients Recognition through Multi-label LearningCode0
Multi-Label Noise Robust Collaborative Learning for Remote Sensing Image ClassificationCode0
Variational Autoencoders for Sparse and Overdispersed Discrete DataCode0
Deep Extreme Multi-label LearningCode0
IDEA: Increasing Text Diversity via Online Multi-Label Recognition for Vision-Language Pre-trainingCode0
Incomplete Multi-View Weak-Label Learning with Noisy Features and Imbalanced LabelsCode0
Neural Collapse in Multi-label Learning with Pick-all-label LossCode0
Label Structure Preserving Contrastive Embedding for Multi-Label Learning with Missing LabelsCode0
Deep Determinantal Point Process for Large-Scale Multi-Label Classification—0
Asymptotic consistency and order specification for logistic classifier chains in multi-label learning—0
The Overlooked Classifier in Human-Object Interaction Recognition—0
Asymmetric Co-teaching with Multi-view Consensus for Noisy Label Learning—0
CRAFTML, an Efficient Clustering-based Random Forest for Extreme Multi-label Learning—0
A Survey on Incomplete Multi-label Learning: Recent Advances and Future Trends—0
Copula Multi-label Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SADCLCF179.8—Unverified