A survey of predictive modeling on imbalanced domains

P Branco, L Torgo, RP Ribeiro - ACM computing surveys (CSUR), 2016 - dl.acm.org
Many real-world data-mining applications involve obtaining predictive models using
datasets with strongly imbalanced distributions of the target variable. Frequently, the least …

A systematic review of detecting sleep apnea using deep learning

SS Mostafa, F Mendonça, A G. Ravelo-García… - Sensors, 2019 - mdpi.com
Sleep apnea is a sleep related disorder that significantly affects the population.
Polysomnography, the gold standard, is expensive, inaccessible, uncomfortable and an …

Long-tail learning via logit adjustment

AK Menon, S Jayasumana, AS Rawat, H Jain… - arXiv preprint arXiv …, 2020 - arxiv.org
Real-world classification problems typically exhibit an imbalanced or long-tailed label
distribution, wherein many labels are associated with only a few samples. This poses a …

Contrastive learning based hybrid networks for long-tailed image classification

P Wang, K Han, XS Wei, L Zhang… - Proceedings of the …, 2021 - openaccess.thecvf.com
Learning discriminative image representations plays a vital role in long-tailed image
classification because it can ease the classifier learning in imbalanced cases. Given the …

A comprehensive analysis of synthetic minority oversampling technique (SMOTE) for handling class imbalance

D Elreedy, AF Atiya - Information Sciences, 2019 - Elsevier
Imbalanced classification problems are often encountered in many applications. The
challenge is that there is a minority class that has typically very little data and is often the …

Resampling imbalanced data for network intrusion detection datasets

S Bagui, K Li - Journal of Big Data, 2021 - Springer
Abstract Machine learning plays an increasingly significant role in the building of Network
Intrusion Detection Systems. However, machine learning models trained with imbalanced …

On the class overlap problem in imbalanced data classification

P Vuttipittayamongkol, E Elyan, A Petrovski - Knowledge-based systems, 2021 - Elsevier
Class imbalance is an active research area in the machine learning community. However,
existing and recent literature showed that class overlap had a higher negative impact on the …

A sensitivity analysis of (and practitioners' guide to) convolutional neural networks for sentence classification

Y Zhang, B Wallace - arXiv preprint arXiv:1510.03820, 2015 - arxiv.org
Convolutional Neural Networks (CNNs) have recently achieved remarkably strong
performance on the practically important task of sentence classification (kim 2014 …

SMOTE for high-dimensional class-imbalanced data

R Blagus, L Lusa - BMC bioinformatics, 2013 - Springer
Background Classification using class-imbalanced data is biased in favor of the majority
class. The bias is even larger for high-dimensional data, where the number of variables …

The class imbalance problem in deep learning

K Ghosh, C Bellinger, R Corizzo, P Branco… - Machine Learning, 2024 - Springer
Deep learning has recently unleashed the ability for Machine learning (ML) to make
unparalleled strides. It did so by confronting and successfully addressing, at least to a …