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 126–150 of 299 papers

TitleStatusHype
Fast Multi-label Learning—0
SCIDA: Self-Correction Integrated Domain Adaptation from Single- to Multi-label Aerial ImagesCode0
Statistical Dependency Guided Contrastive Learning for Multiple Labeling in Prenatal Ultrasound—0
Multi-Label Gold Asymmetric Loss Correction with Single-Label Regulators—0
T_kML-AP: Adversarial Attacks to Top-k Multi-Label LearningCode0
Attack Transferability Characterization for Adversarially Robust Multi-label ClassificationCode0
Extreme Multi-label Learning for Semantic Matching in Product SearchCode0
Sum of Ranked Range Loss for Supervised LearningCode0
Basic and Depression Specific Emotion Identification in Tweets: Multi-label Classification Experiments—0
CCMN: A General Framework for Learning with Class-Conditional Multi-Label Noise—0
A Consensual Collaborative Learning Method for Remote Sensing Image Classification Under Noisy Multi-LabelsCode0
Hierarchical Relationship Alignment Metric Learning—0
Representation Learning by Ranking under multiple tasks—0
Unsupervised Person Re-Identification with Multi-Label Learning Guided Self-Paced Clustering—0
Learning Discriminative Features using Multi-label Dual Space—0
Discovering Multi-Label Actor-Action Association in a Weakly Supervised SettingCode0
TkML-AP: Adversarial Attacks to Top-k Multi-Label LearningCode0
Improving Tail Label Prediction for Extreme Multi-label Learning—0
Multi-Label Noise Robust Collaborative Learning for Remote Sensing Image ClassificationCode0
Towards Coarse and Fine-grained Multi-Graph Multi-Label Learning—0
Characterizing the Evasion Attackability of Multi-label Classifiers—0
A Study on the Autoregressive and non-Autoregressive Multi-label Learning—0
The Emerging Trends of Multi-Label Learning—0
Multi-typed Objects Multi-view Multi-instance Multi-label Learning—0
Learning by Minimizing the Sum of Ranked RangeCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SADCLCF179.8—Unverified