SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary
The Synthetic Minority Oversampling Technique (SMOTE) preprocessing algorithm is
considered" de facto" standard in the framework of learning from imbalanced data. This 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
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 …
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
The theoretical developments of data-driven fault diagnosis methods have yielded fruitful
achievements and significantly benefited industry practices. However, most methods are …
achievements and significantly benefited industry practices. However, most methods are …
Software defect prediction using ensemble learning on selected features
Context Several issues hinder software defect data including redundancy, correlation,
feature irrelevance and missing samples. It is also hard to ensure balanced distribution …
feature irrelevance and missing samples. It is also hard to ensure balanced distribution …
A systematic study of online class imbalance learning with concept drift
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 …
challenges of both class imbalance and concept drift. It deals with data streams having very …
Resampling-based ensemble methods for online class imbalance learning
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 …
both online learning and class imbalance learning. It deals with data streams having very …
Online defect prediction for imbalanced data
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 …
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
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 …
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
In severe data imbalance scenarios, fault samples are generally scarce, challenging the
health management of industrial machinery significantly. Generative adversarial network …
health management of industrial machinery significantly. Generative adversarial network …
Credit risk prediction in an imbalanced social lending environment
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 …
repay a loan, particularly in peer-to-peer lending where class imbalance problems are …