Stop Oversampling for Class Imbalance Learning: A Review
IEEE Access, 2022 For the last two decades, oversampling has been employed to overcome the challenge of learning from imbalanced datasets. Many approaches to solving this challenge have been offered in the literature. Oversampling, on the other hand, is a concern. That is,
Ahmad S. Tarawneh +3 more
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A Machine Learning-Based Water Potability Prediction Model by Using Synthetic Minority Oversampling Technique and Explainable AI [PDF]
Computational Intelligence and Neuroscience, 2022 During the last few decades, the quality of water has deteriorated significantly due to pollution and many other issues. As a consequence of this, there is a need for a model that can make accurate projections about water quality.
J. Patel +7 more
semanticscholar +2 more sources
Oversampling for Imbalanced Learning Based on K-Means and SMOTE [PDF]
Information Sciences, 2017 Learning from class-imbalanced data continues to be a common and challenging problem in supervised learning as standard classification algorithms are designed to handle balanced class distributions. While different strategies exist to tackle this problem,
F. Last, Georgios Douzas, F. Bação
semanticscholar +3 more sources
An Adversary Model of Fraudsters’ Behavior to Improve Oversampling in Credit Card Fraud Detection [PDF]
IEEE Access, 2023 Imbalanced learning jeopardizes the accuracy of traditional classification models, particularly for what concerns the minority class, which is often the class of interest.
Daniele Lunghi +3 more
semanticscholar +2 more sources
A comprehensive evaluation of oversampling techniques for enhancing text classification performance. [PDF]
Sci RepClass imbalance is a common and critical challenge in text classification tasks, where the underrepresentation of certain classes often impairs the ability of classifiers to learn minority class patterns effectively. According to the “garbage in, garbage
Taskiran SF +3 more
europepmc +2 more sources
Physics-Based Spatial Oversampling of TROPOMI NO<sub>2</sub> Observations to US Neighborhoods Reveals the Disparities of Air Pollution. [PDF]
GeohealthJin X, Merchant Z, Sun K.
europepmc +2 more sources
Selective oversampling approach for strongly imbalanced data [PDF]
PeerJ Computer Science, 2021 Challenges posed by imbalanced data are encountered in many real-world applications. One of the possible approaches to improve the classifier performance on imbalanced data is oversampling.
Peter Gnip +2 more
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Tạp chí Khoa họcThis paper proposes a method to enhance the effectiveness of classifying imbalanced data. The main contribution of the method is integrating the K-means clustering algorithm and the minority oversampling technique VCIR to generate synthetic samples that ...
Phan Anh Phong, Le Van Thanh
doaj +2 more sources
IEEE Access, 2022 Imbalanced datasets are pervasive in classification tasks and would cause degradation of the performance of classifiers in predicting minority samples. Oversampling is effective in resolving the class imbalance problem.
Changhui Liu +6 more
doaj +1 more source
The Majority Can Help the Minority: Context-rich Minority Oversampling for Long-tailed Classification [PDF]
Computer Vision and Pattern Recognition, 2021 The problem of class imbalanced data is that the gener-alization performance of the classifier deteriorates due to the lack of data from minority classes.
Seulki Park +4 more
semanticscholar +1 more source