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
A broad review on class imbalance learning techniques
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TL;DR: In this article , a review of existing methods to deal with issues linked with class imbalance learning is presented, and a taxonomy for class imbalanced learning techniques is proposed and classified into three parts: (1) Data pre-processing, (2) Algorithmic structures, and (3) Hybrid techniques.
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FW-SMOTE: A feature-weighted oversampling approach for imbalanced classification
TL;DR: In this article , a weighted Minkowski distance is used to define the neighborhood for each example of the minority class, which leads to a better definition of the neighborhood since it prioritizes those features that are more relevant for the classification task.
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Gaussian Distribution based Oversampling for Imbalanced Data Classification
TL;DR: Experimental results show that the proposed Gaussian Distribution based Oversampling (GDO) method outperforms the other compared methods in terms of AUC, G-mean and memory usage with an increase in running time and the experimental results once again validate the effectiveness of the approach.
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COSTE: Complexity-based OverSampling TEchnique to alleviate the class imbalance problem in software defect prediction
TL;DR: Complexity-based OverSampling TEchnique (COSTE), a novel oversampling technique that can achieve low p f and high p d simultaneously, is introduced and is recommended as an efficient alternative to address the class imbalance problem in SDP.
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New imbalanced fault diagnosis framework based on Cluster-MWMOTE and MFO-optimized LS-SVM using limited and complex bearing data
TL;DR: A new imbalanced fault diagnosis framework based on a cluster-majority weighted minority oversampling technique (Cluster-MWMOTE) and a moth-flame optimization (MFO)-based LS-SVM classifier, which improves the adaptation to within-class imbalances.
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References
SMOTE: synthetic minority over-sampling technique
TL;DR: In this article, a method of over-sampling the minority class involves creating synthetic minority class examples, which is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.
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