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
Multi-Label Learning to Rank through Multi-Objective Optimization—0
Multi-Label Learning with Deep Forest—0
Multi-Label Learning with Global and Local Label Correlation—0
Multi-Label Learning with Label Enhancement—0
Multi-label Learning with Missing Labels using Mixed Dependency Graphs—0
Multi-label Learning with Missing Values using Combined Facial Action Unit Datasets—0
Multi-Label Learning with Pairwise Relevance Ordering—0
Multi-Label Learning with Provable Guarantee—0
Multi-Label Learning with Stronger Consistency Guarantees—0
MULTI-LABEL METRIC LEARNING WITH BIDIRECTIONAL REPRESENTATION DEEP NEURAL NETWORKS—0
Multi-label Stream Classification with Self-Organizing Maps—0
Multi-label Text Categorization with Joint Learning Predictions-as-Features Method—0
Multi-Label Zero-Shot Human Action Recognition via Joint Latent Ranking Embedding—0
Multi-typed Objects Multi-view Multi-instance Multi-label Learning—0
Multi-View Multi-Instance Multi-Label Learning based on Collaborative Matrix Factorization—0
Noise Mitigation for Neural Entity Typing and Relation Extraction—0
Noise-Resistant Label Reconstruction Feature Selection for Partial Multi-Label Learning—0
Noisy Or-based model for Relation Extraction using Distant Supervision—0
Nonconvex One-bit Single-label Multi-label Learning—0
On a scalable problem transformation method for multi-label learning—0
Online Boosting Algorithms for Multi-label Ranking—0
Overcoming Label Ambiguity with Multi-label Iterated Learning—0
Partial Multi-label Learning with Label and Feature Collaboration—0
Partial Multi-label Learning with Noisy Label Identification—0
Patch Spatio-Temporal Relation Prediction for Video Anomaly Detection—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SADCLCF179.8—Unverified