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 176–200 of 299 papers

TitleStatusHype
Intra-Camera Supervised Person Re-Identification: A New Benchmark—0
Joint Binary Neural Network for Multi-label Learning with Applications to Emotion Classification—0
Joint Patch and Multi-Label Learning for Facial Action Unit Detection—0
KDGAN: Knowledge Distillation with Generative Adversarial Networks—0
Label Distribution Learning—0
Label Distribution Learning via Implicit Distribution Representation—0
Large-Scale Bayesian Multi-Label Learning via Topic-Based Label Embeddings—0
Large-Scale Multi-Label Learning with Incomplete Label Assignments—0
Large-scale Multi-label Learning with Missing Labels—0
Latent Topic-aware Multi-Label Classification—0
LD-SDM: Language-Driven Hierarchical Species Distribution Modeling—0
Learnability Gaps of Strategic Classification—0
Learning Discriminative Features using Multi-label Dual Space—0
Learning Disentangled Label Representations for Multi-label Classification—0
Reliable Representations Learning for Incomplete Multi-View Partial Multi-Label Classification—0
Learning with a Wasserstein Loss—0
Leveraging Distributional Semantics for Multi-Label Learning—0
Locally Non-linear Embeddings for Extreme Multi-label Learning—0
Local Rademacher Complexity for Multi-label Learning—0
Logistic Boosting Regression for Label Distribution Learning—0
Making Classifier Chains Resilient to Class Imbalance—0
Masked Two-channel Decoupling Framework for Incomplete Multi-view Weak Multi-label Learning—0
MetaMIML: Meta Multi-Instance Multi-Label Learning—0
MIML-FCN+: Multi-instance Multi-label Learning via Fully Convolutional Networks with Privileged Information—0
ML-MG: Multi-Label Learning With Missing Labels Using a Mixed Graph—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SADCLCF179.8—Unverified