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:

Kumar, S., Rastogi, R., Low rank label subspace transformation for multi-label learning with missing labels. Information Sciences 596, 53–72 (2022)
Zhang M-L, Zhou Z-H (2013) A review on multi-label learning algorithms. IEEE Trans Knowl Data Eng 26(8):1819–1837
Gibaja E, Ventura S (2015) A tutorial on multilabel learning. ACM Comput Surveys (CSUR) 47(3):1–38
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
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
Chen Z, Ren J (2021) Multi-label text classification with latent word-wise label information. Appl Intell 51(2):966–979

Title	Date	Tasks	Status
KDGAN: Knowledge Distillation with Generative Adversarial Networks	Dec 1, 2018	Knowledge DistillationMulti-Label Learning	—Unverified
APLenty: annotation tool for creating high-quality datasets using active and proactive learning	Nov 1, 2018	Active LearningMulti-Label Learning	—Unverified
Few-Shot and Zero-Shot Multi-Label Learning for Structured Label Spaces	Oct 1, 2018	General ClassificationMulti-Label Classification	CodeCode Available
An Interpretable Neural Network with Topical Information for Relevant Emotion Ranking	Oct 1, 2018	Common Sense ReasoningEmotion Classification	—Unverified
Making Classifier Chains Resilient to Class Imbalance	Jul 30, 2018	Multi-Label Learning	—Unverified
CRAFTML, an Efficient Clustering-based Random Forest for Extreme Multi-label Learning	Jul 1, 2018	ClusteringMulti-Label Learning	—Unverified
Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling	Jun 26, 2018	compressed sensingDecoder	CodeCode Available
Incremental Sparse Bayesian Ordinal Regression	Jun 18, 2018	Multi-Label Learningregression	CodeCode Available
Relevant Emotion Ranking from Text Constrained with Emotion Relationships	Jun 1, 2018	Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATION	—Unverified
Distribution-based Label Space Transformation for Multi-label Learning	May 15, 2018	Dimensionality ReductionMulti-Label Learning	—Unverified

Title

Status

Hype

KDGAN: Knowledge Distillation with Generative Adversarial Networks

—Unverified

APLenty: annotation tool for creating high-quality datasets using active and proactive learning

—Unverified

Few-Shot and Zero-Shot Multi-Label Learning for Structured Label Spaces

CodeCode Available

An Interpretable Neural Network with Topical Information for Relevant Emotion Ranking

—Unverified

Making Classifier Chains Resilient to Class Imbalance