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 126–150 of 299 papers

TitleStatusHype
Towards Coarse and Fine-grained Multi-Graph Multi-Label Learning—0
Towards Effective Multi-Label Recognition Attacks via Knowledge Graph Consistency—0
Towards Enhanced Classification of Abnormal Lung sound in Multi-breath: A Light Weight Multi-label and Multi-head Attention Classification Method—0
Towards Improved Imbalance Robustness in Continual Multi-Label Learning with Dual Output Spiking Architecture (DOSA)—0
Towards Interpretable Deep Extreme Multi-label Learning—0
Towards Label Imbalance in Multi-label Classification with Many Labels—0
Transduction with Matrix Completion: Three Birds with One Stone—0
Transductive Matrix Completion with Calibration for Multi-Task Learning—0
Uncertainty-Aware Global-View Reconstruction for Multi-View Multi-Label Feature Selection—0
Understanding Label Bias in Single Positive Multi-Label Learning—0
Understanding Partial Multi-Label Learning via Mutual Information—0
Universal Domain Adaptive Object Detector—0
Unsupervised Person Re-Identification with Multi-Label Learning Guided Self-Paced Clustering—0
Variational Label Enhancement—0
View-Category Interactive Sharing Transformer for Incomplete Multi-View Multi-Label Learning—0
Weakly Supervised Dense Video Captioning—0
Weakly-Supervised Multi-Person Action Recognition in 360^ Videos—0
Weakly Supervised Person Re-Identification—0
Distribution-based Label Space Transformation for Multi-label Learning—0
DocTag2Vec: An Embedding Based Multi-label Learning Approach for Document Tagging—0
Dynamic classifier chains for multi-label learning—0
Dynamic Programming for Instance Annotation in Multi-instance Multi-label Learning—0
EDGE: Unknown-aware Multi-label Learning by Energy Distribution Gap Expansion—0
Emotion Distribution Learning from Texts—0
Evaluation of Joint Multi-Instance Multi-Label Learning For Breast Cancer Diagnosis—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SADCLCF179.8—Unverified