SMOTE: Synthetic Minority Over-sampling Technique

2002/06/01 by Nitesh V. Chawla, N. V. Chawla, K. W. Bowyer +5 · 101 citations
Computer Science · #Anomaly Detection Techniques and Applications #Imbalanced Data Classification Techniques #Text and Document Classification Technologies #cs.AI

paper · pdf · doi:10.1613/jair.953

published as Journal Of Artificial Intelligence Research, Volume 16, pages 321-357, 2002

openalex publication_date 2002/06/01 · arxiv created 2011/06/09 · arxiv updated 2011/11/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04

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.

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