A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework
Class imbalance poses new challenges when it comes to classifying data streams. Many
algorithms recently proposed in the literature tackle this problem using a variety of data …
algorithms recently proposed in the literature tackle this problem using a variety of data …
Class-overlap undersampling based on Schur decomposition for Class-imbalance problems
The class-imbalance problem is an important area that plagues machine learning and data
mining researchers. It is ubiquitous in all areas of the real world. At present, many methods …
mining researchers. It is ubiquitous in all areas of the real world. At present, many methods …
Automatic Quality Assessment of Wikipedia Articles—A Systematic Literature Review
PM Moás, CT Lopes - ACM Computing Surveys, 2023 - dl.acm.org
Wikipedia is the world's largest online encyclopedia, but maintaining article quality through
collaboration is challenging. Wikipedia designed a quality scale, but with such a manual …
collaboration is challenging. Wikipedia designed a quality scale, but with such a manual …
Epileptic eeg classification by using time-frequency images for deep learning
Epilepsy is one of the most common brain disorders worldwide. The most frequently used
clinical tool to detect epileptic events and monitor epilepsy patients is the EEG recordings …
clinical tool to detect epileptic events and monitor epilepsy patients is the EEG recordings …
DBIG-US: A two-stage under-sampling algorithm to face the class imbalance problem
The class imbalance problem occurs when one class far outnumbers the other classes,
causing most traditional classifiers perform poorly on the minority classes. To tackle this …
causing most traditional classifiers perform poorly on the minority classes. To tackle this …
[PDF][PDF] Comparison of ensemble hybrid sampling with bagging and boosting machine learning approach for imbalanced data
NHA Malek, WFW Yaacob, YB Wah… - Indones. J. Elec. Eng …, 2023 - academia.edu
Training an imbalanced dataset can cause classifiers to overfit the majority class and
increase the possibility of information loss for the minority class. Moreover, accuracy may not …
increase the possibility of information loss for the minority class. Moreover, accuracy may not …
Machine learning based on resampling approaches and deep reinforcement learning for credit card fraud detection systems
The problem of imbalanced datasets is a significant concern when creating reliable credit
card fraud (CCF) detection systems. In this work, we study and evaluate recent advances in …
card fraud (CCF) detection systems. In this work, we study and evaluate recent advances in …
Arrhythmic heartbeat classification using 2d convolutional neural networks
Background Electrocardiogram (ECG) is a method of recording the electrical activity of the
heart and it provides a diagnostic means for heart-related diseases. Arrhythmia is any …
heart and it provides a diagnostic means for heart-related diseases. Arrhythmia is any …
A novel ensemble framework for an intelligent intrusion detection system
Background: Building an effective Intrusion detection system in a multi-attack classification
environment is challenging due to the diversity of modern, sophisticated attacks. High …
environment is challenging due to the diversity of modern, sophisticated attacks. High …
Data augmentation for rolling bearing fault diagnosis using an enhanced few-shot Wasserstein auto-encoder with meta-learning
Z Pei, H Jiang, X Li, J Zhang, S Liu - Measurement Science and …, 2021 - iopscience.iop.org
Despite the advance of intelligent fault diagnosis for rolling bearings, in industries, data-
driven methods still suffer from data acquisition and imbalance. We propose an enhanced …
driven methods still suffer from data acquisition and imbalance. We propose an enhanced …