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 151–175 of 299 papers

TitleStatusHype
Compact Learning for Multi-Label Classification—0
Semi-Supervised Active Learning for COVID-19 Lung Ultrasound Multi-symptom Classification—0
Positive semidefinite support vector regression metric learning—0
Multi-label Learning with Missing Values using Combined Facial Action Unit Datasets—0
SPL-MLL: Selecting Predictable Landmarks for Multi-Label Learning—0
RAF-AU Database: In-the-Wild Facial Expressions with Subjective Emotion Judgement and Objective AU Annotations—0
Latent Topic-aware Multi-Label Classification—0
Bidirectional Loss Function for Label Enhancement and Distribution Learning—0
Recovering Accurate Labeling Information from Partially Valid Data for Effective Multi-Label Learning—0
Multi-Instance Multi-Label Learning for Gene Mutation Prediction in Hepatocellular Carcinoma—0
Multi-label Stream Classification with Self-Organizing Maps—0
Incorporating Multiple Cluster Centers for Multi-Label Learning—0
MLPSVM:A new parallel support vector machine to multi-label learning—0
Expand Globally, Shrink Locally: Discriminant Multi-label Learning with Missing Labels—0
Partial Multi-label Learning with Noisy Label Identification—0
Multi-label learning for dynamic model type recommendationCode0
Partial Multi-label Learning with Label and Feature Collaboration—0
Deep Multi-task Multi-label CNN for Effective Facial Attribute Classification—0
Weakly-Supervised Multi-Person Action Recognition in 360^ Videos—0
Adversarial Paritial Multi-label Learning—0
Variational Label Enhancement—0
Deep Streaming Label LearningCode0
Financial News Annotation by Weakly-Supervised Hierarchical Multi-label Learning—0
Classifier Chains: A Review and Perspectives—0
On-the-fly Global Embeddings Using Random Projections for Extreme Multi-label Classification—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SADCLCF179.8—Unverified