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 101–125 of 299 papers

TitleStatusHype
Incomplete Multi-View Weak-Label Learning with Noisy Features and Imbalanced LabelsCode0
The Overlooked Classifier in Human-Object Interaction Recognition—0
Simple and Robust Loss Design for Multi-Label Learning with Missing LabelsCode1
Improving Predictions of Tail-end Labels using Concatenated BioMed-Transformers for Long Medical DocumentsCode0
Multi-Label Learning with Pairwise Relevance Ordering—0
Understanding Partial Multi-Label Learning via Mutual Information—0
Multi-label Iterated Learning for Image Classification with Label AmbiguityCode0
DeepXML: A Deep Extreme Multi-Label Learning Framework Applied to Short Text DocumentsCode1
MetaMIML: Meta Multi-Instance Multi-Label Learning—0
A Multi-label Multi-hop Relation Detection Model based on Relation-aware Sequence Generation—0
A Simple Approach to Image Tilt Correction with Self-Attention MobileNet for Smartphones—0
Overcoming Label Ambiguity with Multi-label Iterated Learning—0
Active Refinement for Multi-Label Learning: A Pseudo-Label Approach—0
Boost-RS: Boosted Embeddings for Recommender Systems and its Application to Enzyme-Substrate Interaction PredictionCode0
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
DECAF: Deep Extreme Classification with Label FeaturesCode1
T_kML-AP: Adversarial Attacks to Top-k Multi-Label LearningCode0
ECLARE: Extreme Classification with Label Graph CorrelationsCode1
XFL: Naming Functions in Binaries with Extreme Multi-label LearningCode1
MLPD: Multi-Label Pedestrian Detector in Multispectral DomainCode1
Attack Transferability Characterization for Adversarially Robust Multi-label ClassificationCode0
Extreme Multi-label Learning for Semantic Matching in Product Search—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SADCLCF179.8—Unverified