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
Compact Learning for Multi-Label Classification—0
Comparing and combining classifiers for self-taught vocal interfaces—0
Component-Wise Boosting of Targets for Multi-Output Prediction—0
Context-Based Semantic-Aware Alignment for Semi-Supervised Multi-Label Learning—0
Can Class-Priors Help Single-Positive Multi-Label Learning?—0
Copula Multi-label Learning—0
CRAFTML, an Efficient Clustering-based Random Forest for Extreme Multi-label Learning—0
A Survey on Incomplete Multi-label Learning: Recent Advances and Future Trends—0
A Pseudo-Label Method for Coarse-to-Fine Multi-Label Learning with Limited Supervision—0
The Overlooked Classifier in Human-Object Interaction Recognition—0
Deep Determinantal Point Process for Large-Scale Multi-Label Classification—0
Asymptotic consistency and order specification for logistic classifier chains in multi-label learning—0
EDGE: Unknown-aware Multi-label Learning by Energy Distribution Gap Expansion—0
Adversarial Partial Multi-Label Learning—0
A Procedural Texture Generation Framework Based on Semantic Descriptions—0
Action Unit Detection with Region Adaptation, Multi-labeling Learning and Optimal Temporal Fusing—0
Emotion Distribution Learning from Texts—0
Evolving Text Data Stream Mining—0
Bidirectional Loss Function for Label Enhancement and Distribution Learning—0
Bayesian Network Based Label Correlation Analysis For Multi-label Classifier Chain—0
APLenty: annotation tool for creating high-quality datasets using active and proactive learning—0
Determined Multi-Label Learning via Similarity-Based Prompt—0
DeepXML: Scalable & Accurate Deep Extreme Classification for Matching User Queries to Advertiser Bid Phrases—0
Basic and Depression Specific Emotion Identification in Tweets: Multi-label Classification Experiments—0
Deep Topic Models for Multi-label Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SADCLCF179.8—Unverified