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	Hype
Towards Interpretable Deep Extreme Multi-label Learning	Jul 3, 2019	BIG-bench Machine LearningMulti-Label Learning	—Unverified	0
A Submodular Feature-Aware Framework for Label Subset Selection in Extreme Classification Problems	Jun 1, 2019	General ClassificationMulti-Label Learning	—Unverified	0
Atlas of Digital Pathology: A Generalized Hierarchical Histological Tissue Type-Annotated Database for Deep Learning	Jun 1, 2019	DiagnosticMulti-Label Learning	CodeCode Available	0
On a scalable problem transformation method for multi-label learning	May 27, 2019	Binary ClassificationGeneral Classification	—Unverified	0
Multi-View Multi-Instance Multi-Label Learning based on Collaborative Matrix Factorization	May 13, 2019	Multi-Label Learning	—Unverified	0
Synthetic Oversampling of Multi-Label Data based on Local Label Distribution	May 2, 2019	Multi-Label Learning	CodeCode Available	1
Variational Autoencoders for Sparse and Overdispersed Discrete Data	May 2, 2019	Collaborative FilteringMulti-Label Learning	CodeCode Available	0
Bonsai -- Diverse and Shallow Trees for Extreme Multi-label Classification	Apr 17, 2019	ClassificationExtreme Multi-Label Classification	CodeCode Available	0
Robust and Discriminative Labeling for Multi-label Active Learning Based on Maximum Correntropy Criterion	Apr 14, 2019	Active LearningMulti-Label Learning	—Unverified	0
Deep Topic Models for Multi-label Learning	Apr 13, 2019	Multi-Label LearningTopic Models	—Unverified	0

Title

Status

Hype

Towards Interpretable Deep Extreme Multi-label Learning

—Unverified

A Submodular Feature-Aware Framework for Label Subset Selection in Extreme Classification Problems

—Unverified

Atlas of Digital Pathology: A Generalized Hierarchical Histological Tissue Type-Annotated Database for Deep Learning

CodeCode Available

On a scalable problem transformation method for multi-label learning

—Unverified

Multi-View Multi-Instance Multi-Label Learning based on Collaborative Matrix Factorization