A review of methods for imbalanced multi-label classification

AN Tarekegn, M Giacobini, K Michalak - Pattern Recognition, 2021 - Elsevier
Abstract Multi-Label Classification (MLC) is an extension of the standard single-label
classification where each data instance is associated with several labels simultaneously …

Tomek link and SMOTE approaches for machine fault classification with an imbalanced dataset

EF Swana, W Doorsamy, P Bokoro - Sensors, 2022 - mdpi.com
Data-driven methods have prominently featured in the progressive research and
development of modern condition monitoring systems for electrical machines. These …

Effective class-imbalance learning based on SMOTE and convolutional neural networks

JH Joloudari, A Marefat, MA Nematollahi, SS Oyelere… - Applied Sciences, 2023 - mdpi.com
Imbalanced Data (ID) is a problem that deters Machine Learning (ML) models from
achieving satisfactory results. ID is the occurrence of a situation where the quantity of the …

Towards class-imbalance aware multi-label learning

ML Zhang, YK Li, H Yang, XY Liu - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Multi-label learning deals with training examples each represented by a single instance
while associated with multiple class labels. Due to the exponential number of possible label …

A novel approach for fraudulent reviewer detection based on weighted topic modelling and nearest neighbors with asymmetric Kullback–Leibler divergence

W Zhang, R Xie, Q Wang, Y Yang, J Li - Decision Support Systems, 2022 - Elsevier
The task of detecting fraudulent reviewers is of great importance to E-commerce platforms.
Existing research has invested much effort into developing comprehensive features and …

On supervised class-imbalanced learning: An updated perspective and some key challenges

S Das, SS Mullick, I Zelinka - IEEE Transactions on Artificial …, 2022 - ieeexplore.ieee.org
The problem of class imbalance has always been considered as a significant challenge to
traditional machine learning and the emerging deep learning research communities. A …

Multi-label borderline oversampling technique

Z Teng, P Cao, M Huang, Z Gao, X Wang - Pattern Recognition, 2024 - Elsevier
Class imbalance problem commonly exists in multi-label classification (MLC) tasks. It has
non-negligible impacts on the classifier performance and has drawn extensive attention in …

多标签文本分类研究回顾与展望.

张文峰, 奚雪峰, 崔志明, 邹逸晨… - Journal of Computer …, 2023 - search.ebscohost.com
文本分类(TC) 是自然语言处理(NLP) 领域的重要基础任务, 多标签文本分类(MLTC) 是TC
的重要分支. 为了对多标签文本分类领域进行深入了解, 介绍了多标签文本分类的概念和流程 …

A diversity and reliability-enhanced synthetic minority oversampling technique for multi-label learning

Y Gong, Q Wu, M Zhou, C Chen - Information Sciences, 2025 - Elsevier
The class imbalance issue is generally intrinsic in multi-label datasets due to the fact that
they have a large number of labels and each sample is associated with only a few of them …

Toward hierarchical classification of imbalanced data using random resampling algorithms

RM Pereira, YMG Costa, CN Silla Jr - Information Sciences, 2021 - Elsevier
Although the class imbalance issue affects hierarchical datasets, the literature has few
studies that deal with it, especially when it comes to methods to pre-process the training …