SMOTE: Synthetic Minority Over-sampling Technique
N. V. Chawla, K. W. Bowyer, L. O. Hall, W. P. Kegelmeyer
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- github.com/Taoudi/DataAugmentationtf★ 1
- github.com/brendankowitz/machine-learning-csharpnone★ 0
- github.com/Taoudi/ImbalancedDatatf★ 0
- github.com/basiralab/MV-LEAPnone★ 0
- github.com/MouadNid01/Credit-card-fraud-detection-with-XGBoostnone★ 0
- github.com/RushikeshNaidu/Machine-Estimation-of-Exposure---Massdeptf★ 0
- github.com/chingisooinar/SMOTE-Pytorchpytorch★ 0
- github.com/earthat/SMOTE-over-Samplingnone★ 0
- github.com/MindSpore-scientific-2/code-10/tree/main/SMOTEmindspore★ 0
- github.com/Stamatis-Ilias/PLAsTiCC-Astronomical-Classificationnone★ 0
Abstract
An approach to the construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification categories are not approximately equally represented. Often real-world data sets are predominately composed of "normal" examples with only a small percentage of "abnormal" or "interesting" examples. It is also the case that the cost of misclassifying an abnormal (interesting) example as a normal example is often much higher than the cost of the reverse error. Under-sampling of the majority (normal) class has been proposed as a good means of increasing the sensitivity of a classifier to the minority class. This paper shows that a combination of our method of over-sampling the minority (abnormal) class and under-sampling the majority (normal) class can achieve better classifier performance (in ROC space) than only under-sampling the majority class. This paper also shows that a combination of our method of over-sampling the minority class and under-sampling the majority class can achieve better classifier performance (in ROC space) than varying the loss ratios in Ripper or class priors in Naive Bayes. Our method of over-sampling the minority class involves creating synthetic minority class examples. Experiments are performed using C4.5, Ripper and a Naive Bayes classifier. The method is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.