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 76–100 of 299 papers

TitleStatusHype
Probing Sentiment-Oriented Pre-Training Inspired by Human Sentiment Perception MechanismCode0
Asymmetric Co-teaching with Multi-view Consensus for Noisy Label Learning—0
RLSEP: Learning Label Ranks for Multi-label ClassificationCode0
Learning Disentangled Label Representations for Multi-label Classification—0
A Cross-Conformal Predictor for Multi-label Classification—0
Private Multi-Winner Voting for Machine Learning—0
TabMixer: Excavating Label Distribution Learning with Small-scale Features—0
Food Ingredients Recognition through Multi-label LearningCode0
A Survey on Extreme Multi-label LearningCode1
Label Distribution Learning via Implicit Distribution Representation—0
Single-Stage Broad Multi-Instance Multi-Label Learning (BMIML) with Diverse Inter-Correlations and its application to medical image classification—0
Label Structure Preserving Contrastive Embedding for Multi-Label Learning with Missing LabelsCode0
From Multi-label Learning to Cross-Domain Transfer: A Model-Agnostic Approach—0
IDEA: Increasing Text Diversity via Online Multi-Label Recognition for Vision-Language Pre-trainingCode0
Towards Effective Multi-Label Recognition Attacks via Knowledge Graph Consistency—0
Multi-Label Learning to Rank through Multi-Objective Optimization—0
Universal Domain Adaptive Object Detector—0
Discriminatory Label-specific Weights for Multi-label Learning with Missing LabelsCode0
One Positive Label is Sufficient: Single-Positive Multi-Label Learning with Label EnhancementCode1
Object-Aware Self-supervised Multi-Label LearningCode0
Random Manifold Sampling and Joint Sparse Regularization for Multi-label Feature Selection—0
Acknowledging the Unknown for Multi-label Learning with Single Positive LabelsCode1
The Overlooked Classifier in Human-Object Interaction Recognition—0
Low rank label subspace transformation for multi-label learning with missing labelsCode0
A Similarity-based Framework for Classification Task—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SADCLCF179.8—Unverified