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Multi-Label Learning
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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:
Kumar, S., Rastogi, R., Low rank label subspace transformation for multi-label learning with missing labels. Information Sciences 596, 53–72 (2022)
Zhang M-L, Zhou Z-H (2013) A review on multi-label learning algorithms. IEEE Trans Knowl Data Eng 26(8):1819–1837
Gibaja E, Ventura S (2015) A tutorial on multilabel learning. ACM Comput Surveys (CSUR) 47(3):1–38
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
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
Chen Z, Ren J (2021) Multi-label text classification with latent word-wise label information. Appl Intell 51(2):966–979
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Exploring Partial Multi-Label Learning via Integrating Semantic Co-occurrence Knowledge
When and How Unlabeled Data Provably Improve In-Context Learning
Noise-Resistant Label Reconstruction Feature Selection for Partial Multi-Label Learning
L3A: Label-Augmented Analytic Adaptation for Multi-Label Class Incremental Learning
Cut out and Replay: A Simple yet Versatile Strategy for Multi-Label Online Continual Learning
When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification
Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data
Conformal Prediction with Corrupted Labels: Uncertain Imputation and Robust Re-weighting
FairPO: Robust Preference Optimization for Fair Multi-Label Learning
Model Evaluation in the Dark: Robust Classifier Metrics with Missing Labels
GPMFS: Global Foundation and Personalized Optimization for Multi-Label Feature Selection
Deep Learning Approaches for Medical Imaging Under Varying Degrees of Label Availability: A Comprehensive Survey
ProtoECGNet: Case-Based Interpretable Deep Learning for Multi-Label ECG Classification with Contrastive Learning
Multi-label feature selection based on binary hashing learning and dynamic graph constraints
Uncertainty-Aware Global-View Reconstruction for Multi-View Multi-Label Feature Selection
GSBA$^K$: $top$-$K$ Geometric Score-based Black-box Attack
Label Ranker: Self-Aware Preference for Classification Label Position in Visual Masked Self-Supervised Pre-Trained Model
Improving Multi-Label Contrastive Learning by Leveraging Label Distribution
A Simple but Effective Closed-form Solution for Extreme Multi-label Learning
Context-Based Semantic-Aware Alignment for Semi-Supervised Multi-Label Learning
Towards Macro-AUC oriented Imbalanced Multi-Label Continual Learning
Exploiting Label Skewness for Spiking Neural Networks in Federated Learning
Extreme Multi-label Completion for Semantic Document Labelling with Taxonomy-Aware Parallel Learning
EDGE: Unknown-aware Multi-label Learning by Energy Distribution Gap Expansion
Probably Approximately Precision and Recall Learning
Implicit Regularization for Multi-label Feature Selection
Dual-Label Learning With Irregularly Present Labels
Task-Augmented Cross-View Imputation Network for Partial Multi-View Incomplete Multi-Label Classification
Rethinking Prompting Strategies for Multi-Label Recognition with Partial Annotations
Deep Self-Cleansing for Medical Image Segmentation with Noisy Labels
A Simple and Generalist Approach for Panoptic Segmentation
CLEANANERCorp: Identifying and Correcting Incorrect Labels in the ANERcorp Dataset
Differentiable Logic Programming for Distant Supervision
On the Necessity of World Knowledge for Mitigating Missing Labels in Extreme Classification
From Lazy to Prolific: Tackling Missing Labels in Open Vocabulary Extreme Classification by Positive-Unlabeled Sequence Learning
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