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 226–250 of 299 papers

TitleStatusHype
Joint Binary Neural Network for Multi-label Learning with Applications to Emotion Classification—0
Multi-Label Learning from Medical Plain Text with Convolutional Residual Models—0
Multi-label Learning for Large Text Corpora using Latent Variable Model with Provable Gurantees—0
Similarity-based Multi-label Learning—0
Online Boosting Algorithms for Multi-label Ranking—0
Dynamic classifier chains for multi-label learning—0
Multi-Label Learning of Part Detectors for Heavily Occluded Pedestrian Detection—0
Deep Determinantal Point Process for Large-Scale Multi-Label Classification—0
Leveraging Distributional Semantics for Multi-Label Learning—0
Subset Labeled LDA for Large-Scale Multi-Label Classification—0
Multi-Label Zero-Shot Human Action Recognition via Joint Latent Ranking Embedding—0
Multi-label Class-imbalanced Action Recognition in Hockey Videos via 3D Convolutional Neural Networks—0
Adversarial Training for Relation Extraction—0
Scalable Generative Models for Multi-label Learning with Missing Labels—0
Food Ingredients Recognition through Multi-label LearningCode0
DocTag2Vec: An Embedding Based Multi-label Learning Approach for Document Tagging—0
Multi-Label Learning with Label Enhancement—0
A Procedural Texture Generation Framework Based on Semantic Descriptions—0
Deep Extreme Multi-label LearningCode0
Action Unit Detection with Region Adaptation, Multi-labeling Learning and Optimal Temporal Fusing—0
Weakly Supervised Dense Video Captioning—0
Multi-Label Learning with Global and Local Label Correlation—0
Nonconvex One-bit Single-label Multi-label Learning—0
Semi-supervised multi-label feature selection via label correlation analysis with l1-norm graph embeddingCode0
MIML-FCN+: Multi-instance Multi-label Learning via Fully Convolutional Networks with Privileged Information—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SADCLCF179.8—Unverified