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 176–200 of 299 papers

TitleStatusHype
Single-Stage Broad Multi-Instance Multi-Label Learning (BMIML) with Diverse Inter-Correlations and its application to medical image classification—0
Sparse Local Embeddings for Extreme Multi-label Classification—0
Speedup Matrix Completion with Side Information: Application to Multi-Label Learning—0
SPL-MLL: Selecting Predictable Landmarks for Multi-Label Learning—0
Statistical Dependency Guided Contrastive Learning for Multiple Labeling in Prenatal Ultrasound—0
Streaming Label Learning for Modeling Labels on the Fly—0
Student Performance Prediction with Optimum Multilabel Ensemble Model—0
Submodular Multi-Label Learning—0
Subset Labeled LDA for Large-Scale Multi-Label Classification—0
TabMixer: Excavating Label Distribution Learning with Small-scale Features—0
Task-Augmented Cross-View Imputation Network for Partial Multi-View Incomplete Multi-Label Classification—0
The Emerging Trends of Multi-Label Learning—0
Theoretical Foundations of Forward Feature Selection Methods based on Mutual Information—0
Theory-Inspired Deep Multi-View Multi-Label Learning with Incomplete Views and Noisy Labels—0
The Overlooked Classifier in Human-Object Interaction Recognition—0
Towards Calibrated Multi-label Deep Neural Networks—0
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SADCLCF179.8—Unverified