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 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
Implicit Regularization for Multi-label Feature Selection—0
Improving Multi-Label Contrastive Learning by Leveraging Label Distribution—0
Improving Multi-label Learning with Missing Labels by Structured Semantic Correlations—0
LD-SDM: Language-Driven Hierarchical Species Distribution Modeling—0
Improving Tail Label Prediction for Extreme Multi-label Learning—0
Label Distribution Learning—0
Incomplete Multi-View Multi-label Learning via Disentangled Representation and Label Semantic Embedding—0
An Interpretable Neural Network with Topical Information for Relevant Emotion Ranking—0
Label Distribution Learning via Implicit Distribution Representation—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