Journal Article10.1109/TKDE.2012.232
MWMOTE--Majority Weighted Minority Oversampling Technique for Imbalanced Data Set Learning
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TL;DR: A new method, called Majority Weighted Minority Oversampling TEchnique (MWMOTE), is presented for efficiently handling imbalanced learning problems and is better than or comparable with some other existing methods in terms of various assessment metrics.
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Abstract: Imbalanced learning problems contain an unequal distribution of data samples among different classes and pose a challenge to any classifier as it becomes hard to learn the minority class samples. Synthetic oversampling methods address this problem by generating the synthetic minority class samples to balance the distribution between the samples of the majority and minority classes. This paper identifies that most of the existing oversampling methods may generate the wrong synthetic minority samples in some scenarios and make learning tasks harder. To this end, a new method, called Majority Weighted Minority Oversampling TEchnique (MWMOTE), is presented for efficiently handling imbalanced learning problems. MWMOTE first identifies the hard-to-learn informative minority class samples and assigns them weights according to their euclidean distance from the nearest majority class samples. It then generates the synthetic samples from the weighted informative minority class samples using a clustering approach. This is done in such a way that all the generated samples lie inside some minority class cluster. MWMOTE has been evaluated extensively on four artificial and 20 real-world data sets. The simulation results show that our method is better than or comparable with some other existing methods in terms of various assessment metrics, such as geometric mean (G-mean) and area under the receiver operating curve (ROC), usually known as area under curve (AUC).
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Citations
Highly imbalanced fault classification of wind turbines using data resampling and hybrid ensemble method approach
TL;DR: This study proposes a hybrid ensemble method combining adaptive SMOTE and edited nearest neighbors (ASMOTE-ENN) for highly imbalanced wind turbine fault classification, achieving improved accuracy by reducing noise and generating high-quality synthetic minority class samples.
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Comparison of sampling techniques for imbalanced learning
Ahmet Onur Durahim
- 19 Oct 2016
TL;DR: It is found that classification accuracies of the over-sampled methods are superior to the under-sampling methods and the ADASYN method should be the preferred choice considering both execution times, increase in the data size and classification performance.
An Improved MAHAKIL Oversampling Method for Imbalanced Dataset Classification
TL;DR: In this paper, the authors proposed an algorithm combining the K-means clustering with the MAHAKIL oversampling to improve the recognition rate of positive samples in the process of imbalanced dataset classification.
G-SOMO: An oversampling approach based on self-organized maps and geometric SMOTE
TL;DR: A new oversampling algorithm named G-SOMO is proposed that identifies optimal areas to create artificial data instances in an informed manner and utilizes a geometric region during the data generation process to increase their variability.
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SA-CGAN: An oversampling method based on single attribute guided conditional GAN for multi-class imbalanced learning
01 Feb 2022
TL;DR: In this article , to estimate the mechanism of how each attribute contributes to its label, to explore the potential connection between the two items by conditional generative adversarial networks (CGAN) separately and individually, the constructed new instances are purified by a designed attribute-based minimax filter and the survivors are concatenated to form the eventual generated data.
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References
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