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 1–10 of 299 papers

TitleStatusHype
Exploring Partial Multi-Label Learning via Integrating Semantic Co-occurrence Knowledge—0
Noise-Resistant Label Reconstruction Feature Selection for Partial Multi-Label Learning—0
Cut out and Replay: A Simple yet Versatile Strategy for Multi-Label Online Continual LearningCode0
FairPO: Robust Preference Optimization for Fair Multi-Label Learning—0
GPMFS: Global Foundation and Personalized Optimization for Multi-Label Feature Selection—0
ProtoECGNet: Case-Based Interpretable Deep Learning for Multi-Label ECG Classification with Contrastive LearningCode1
Multi-label feature selection based on binary hashing learning and dynamic graph constraints—0
Uncertainty-Aware Global-View Reconstruction for Multi-View Multi-Label Feature Selection—0
GSBA^K: top-K Geometric Score-based Black-box Attack—0
Label Ranker: Self-Aware Preference for Classification Label Position in Visual Masked Self-Supervised Pre-Trained ModelCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SADCLCF179.8—Unverified