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 201–225 of 299 papers

TitleStatusHype
Muli-label Text Categorization with Hidden Components—0
Multi-instance Multi-label Learning for Relation Extraction—0
Multi-Instance Multi-Label Learning for Gene Mutation Prediction in Hepatocellular Carcinoma—0
Multi-Label Adversarial Perturbations—0
Multi-Label Classifier Chains for Bird Sound—0
Multi-label Class-imbalanced Action Recognition in Hockey Videos via 3D Convolutional Neural Networks—0
Multi-label Class Incremental Emotion Decoding with Augmented Emotional Semantics Learning—0
Multi-Labeled Classification of Demographic Attributes of Patients: a case study of diabetics patients—0
Multi-label feature selection based on binary hashing learning and dynamic graph constraints—0
Multi-Label Gold Asymmetric Loss Correction with Single-Label Regulators—0
Multi-label Learning Based Deep Transfer Neural Network for Facial Attribute Classification—0
Multi-label Learning for Large Text Corpora using Latent Variable Model with Provable Gurantees—0
Multi-Label Learning from Medical Plain Text with Convolutional Residual Models—0
Multi-label Learning from Privacy-Label—0
Multi-Label Learning of Part Detectors for Heavily Occluded Pedestrian Detection—0
Multi-Label Learning to Rank through Multi-Objective Optimization—0
Multi-Label Learning with Deep Forest—0
Multi-Label Learning with Global and Local Label Correlation—0
Multi-Label Learning with Label Enhancement—0
Multi-label Learning with Missing Labels using Mixed Dependency Graphs—0
Multi-label Learning with Missing Values using Combined Facial Action Unit Datasets—0
Multi-Label Learning with Pairwise Relevance Ordering—0
Multi-Label Learning with Provable Guarantee—0
Multi-Label Learning with Stronger Consistency Guarantees—0
MULTI-LABEL METRIC LEARNING WITH BIDIRECTIONAL REPRESENTATION DEEP NEURAL NETWORKS—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SADCLCF179.8—Unverified