A survey of predictive modeling on imbalanced domains
Many real-world data-mining applications involve obtaining predictive models using
datasets with strongly imbalanced distributions of the target variable. Frequently, the least …
datasets with strongly imbalanced distributions of the target variable. Frequently, the least …
A systematic review of detecting sleep apnea using deep learning
Sleep apnea is a sleep related disorder that significantly affects the population.
Polysomnography, the gold standard, is expensive, inaccessible, uncomfortable and an …
Polysomnography, the gold standard, is expensive, inaccessible, uncomfortable and an …
Long-tail learning via logit adjustment
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 …
distribution, wherein many labels are associated with only a few samples. This poses a …
Contrastive learning based hybrid networks for long-tailed image classification
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 …
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 …
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 …
Intrusion Detection Systems. However, machine learning models trained with imbalanced …
On the class overlap problem in imbalanced data classification
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 …
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 …
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 …
class. The bias is even larger for high-dimensional data, where the number of variables …
The class imbalance problem in deep learning
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 …
unparalleled strides. It did so by confronting and successfully addressing, at least to a …