SMOTE: Synthetic Minority Over-sampling Technique

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SMOTE: Synthetic Minority Over-sampling Technique (2002)

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by Nitesh V. Chawla , Kevin W. Bowyer , Lawrence O. Hall , W. Philip Kegelmeyer

Venue:	Journal of Artificial Intelligence Research
Citations:	634 - 27 self

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@ARTICLE{Chawla02smote:synthetic,
    author = {Nitesh V. Chawla and Kevin W. Bowyer and Lawrence O. Hall and W. Philip Kegelmeyer},
    title = {SMOTE: Synthetic Minority Over-sampling Technique},
    journal = {Journal of Artificial Intelligence Research},
    year = {2002},
    volume = {16},
    pages = {321--357}
}

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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.

Keyphrases

minority class synthetic minority over-sampling technique roc space majority class reverse error small percentage real-world data set class prior synthetic minority class example naive bayes good mean naive bayes classifier normal example imbalanced datasets roc convex hull strategy classification category receiver operating characteristic curve loss ratio

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