SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary

A Fernández, S Garcia, F Herrera, NV Chawla - Journal of artificial …, 2018 - jair.org
The Synthetic Minority Oversampling Technique (SMOTE) preprocessing algorithm is
considered" de facto" standard in the framework of learning from imbalanced data. This is …

[HTML][HTML] A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation

AA Khan, O Chaudhari, R Chandra - Expert Systems with Applications, 2024 - Elsevier
Class imbalance (CI) in classification problems arises when the number of observations
belonging to one class is lower than the other. Ensemble learning combines multiple models …

A systematic review on imbalanced learning methods in intelligent fault diagnosis

Z Ren, T Lin, K Feng, Y Zhu, Z Liu… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
The theoretical developments of data-driven fault diagnosis methods have yielded fruitful
achievements and significantly benefited industry practices. However, most methods are …

Software defect prediction using ensemble learning on selected features

IH Laradji, M Alshayeb, L Ghouti - Information and Software Technology, 2015 - Elsevier
Context Several issues hinder software defect data including redundancy, correlation,
feature irrelevance and missing samples. It is also hard to ensure balanced distribution …

A systematic study of online class imbalance learning with concept drift

S Wang, LL Minku, X Yao - IEEE transactions on neural …, 2018 - ieeexplore.ieee.org
As an emerging research topic, online class imbalance learning often combines the
challenges of both class imbalance and concept drift. It deals with data streams having very …

Resampling-based ensemble methods for online class imbalance learning

S Wang, LL Minku, X Yao - IEEE Transactions on Knowledge …, 2014 - ieeexplore.ieee.org
Online class imbalance learning is a new learning problem that combines the challenges of
both online learning and class imbalance learning. It deals with data streams having very …

Online defect prediction for imbalanced data

M Tan, L Tan, S Dara, C Mayeux - 2015 IEEE/ACM 37th IEEE …, 2015 - ieeexplore.ieee.org
Many defect prediction techniques are proposed to improve software reliability. Change
classification predicts defects at the change level, where a change is the modifications to …

Discussion and review on evolving data streams and concept drift adapting

I Khamassi, M Sayed-Mouchaweh, M Hammami… - Evolving systems, 2018 - Springer
Recent advances in computational intelligent systems have focused on addressing complex
problems related to the dynamicity of the environments. In increasing number of real world …

Few-shot GAN: Improving the performance of intelligent fault diagnosis in severe data imbalance

Z Ren, Y Zhu, Z Liu, K Feng - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
In severe data imbalance scenarios, fault samples are generally scarce, challenging the
health management of industrial machinery significantly. Generative adversarial network …

Credit risk prediction in an imbalanced social lending environment

A Namvar, M Siami, F Rabhi, M Naderpour - International Journal of …, 2018 - Springer
Credit risk prediction is an effective way of evaluating whether a potential borrower will
repay a loan, particularly in peer-to-peer lending where class imbalance problems are …