2002 SMOTESyntheticMinorityOverSampl

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Subject Headings: SMOTE Algorithm, Imbalanced Data Supervised Classification Algorithm.

Notes

Cited By

2009

  • (He & Garcia, 2009) ⇒ Haibo He, and Edwardo A. Garcia. (2009). “Learning from Imbalanced Data.” In: Knowledge and Data Engineering, IEEE Transactions, 21(9).

Quotes

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 oversampling the minority (abnormal)cla ss and under-sampling the majority (normal) class can achieve better classifier performance (in ROC space)tha n 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)t han 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.


References

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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2002 SMOTESyntheticMinorityOverSamplNitesh V. Chawla
Kevin W. Bowyer
Lawrence O. Hall
W. Philip Kegelmeyer
SMOTE: Synthetic Minority over-sampling Technique2002