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 101–125 of 299 papers

TitleStatusHype
From Multi-label Learning to Cross-Domain Transfer: A Model-Agnostic Approach—0
Expand Globally, Shrink Locally: Discriminant Multi-label Learning with Missing Labels—0
GPMFS: Global Foundation and Personalized Optimization for Multi-Label Feature Selection—0
Graph based Label Enhancement for Multi-instance Multi-label learning—0
Group Preserving Label Embedding for Multi-Label Classification—0
GSBA^K: top-K Geometric Score-based Black-box Attack—0
Hierarchical Multi-Instance Multi-Label Learning for Detecting Propaganda Techniques—0
Hierarchical Partitioning of the Output Space in Multi-label Data—0
Hierarchical Relationship Alignment Metric Learning—0
Holistic Interstitial Lung Disease Detection using Deep Convolutional Neural Networks: Multi-label Learning and Unordered Pooling—0
Advancing Head and Neck Cancer Survival Prediction via Multi-Label Learning and Deep Model Interpretation—0
Latent Topic-aware Multi-Label Classification—0
Improving Multi-Label Contrastive Learning by Leveraging Label Distribution—0
Improving Multi-label Learning with Missing Labels by Structured Semantic Correlations—0
CRAFTML, an Efficient Clustering-based Random Forest for Extreme Multi-label Learning—0
Improving Tail Label Prediction for Extreme Multi-label Learning—0
An Interpretable Neural Network with Topical Information for Relevant Emotion Ranking—0
Incomplete Multi-View Multi-label Learning via Disentangled Representation and Label Semantic Embedding—0
Attention-Based Capsule Networks with Dynamic Routing for Relation Extraction—0
Label Distribution Learning—0
Inferring Restaurant Styles by Mining Crowd Sourced Photos from User-Review Websites—0
Infinite-Label Learning with Semantic Output Codes—0
Intra-Camera Supervised Person Re-Identification: A New Benchmark—0
Joint Binary Neural Network for Multi-label Learning with Applications to Emotion Classification—0
Deep Ranking Based Cost-sensitive Multi-label Learning for Distant Supervision Relation Extraction—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SADCLCF179.8—Unverified