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 51–75 of 299 papers

TitleStatusHype
Boost-RS: Boosted Embeddings for Recommender Systems and its Application to Enzyme-Substrate Interaction PredictionCode0
Learning by Minimizing the Sum of Ranked RangeCode0
Multi-label Iterated Learning for Image Classification with Label AmbiguityCode0
Boosting Single Positive Multi-label Classification with Generalized Robust LossCode0
Bonsai -- Diverse and Shallow Trees for Extreme Multi-label ClassificationCode0
Cost-Sensitive Reference Pair Encoding for Multi-Label LearningCode0
Label Structure Preserving Contrastive Embedding for Multi-Label Learning with Missing LabelsCode0
Learning to Separate Object Sounds by Watching Unlabeled VideoCode0
Improving Predictions of Tail-end Labels using Concatenated BioMed-Transformers for Long Medical DocumentsCode0
IDEA: Increasing Text Diversity via Online Multi-Label Recognition for Vision-Language Pre-trainingCode0
Food Ingredients Recognition through Multi-label LearningCode0
Deep Double Incomplete Multi-view Multi-label Learning with Incomplete Labels and Missing ViewsCode0
Extreme Multi-label Learning for Semantic Matching in Product SearchCode0
Food Ingredients Recognition through Multi-label LearningCode0
Incremental Sparse Bayesian Ordinal RegressionCode0
Deep Streaming Label LearningCode0
Deep Region and Multi-Label Learning for Facial Action Unit DetectionCode0
Attention-Based Capsule Networks with Dynamic Routing for Relation ExtractionCode0
DiSMEC - Distributed Sparse Machines for Extreme Multi-label ClassificationCode0
Discriminatory Label-specific Weights for Multi-label Learning with Missing LabelsCode0
Auxiliary Label Embedding for Multi-label Learning with Missing LabelsCode0
Dual-Decoupling Learning and Metric-Adaptive Thresholding for Semi-Supervised Multi-Label LearningCode0
A Consensual Collaborative Learning Method for Remote Sensing Image Classification Under Noisy Multi-LabelsCode0
Few-Shot and Zero-Shot Multi-Label Learning for Structured Label SpacesCode0
Atlas of Digital Pathology: A Generalized Hierarchical Histological Tissue Type-Annotated Database for Deep LearningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SADCLCF179.8—Unverified