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SMOTE: SyntheticMinorityOver-samplingTechnique Nitesh V. ChawlaKevin W. BowyerLawrence Hall2002 год

SMOTE: Synthetic Minority Over-sampling Technique
статья из журнала

Полный текстСтраница публикацииПубликация в OpenAlex
Аннотация: 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.
Год издания: 2002
Авторы: Nitesh V. Chawla, Kevin W. Bowyer, Lawrence Hall, W. Philip Kegelmeyer
Издательство: AI Access Foundation
Источник: Journal of Artificial Intelligence Research
Ключевые слова: Imbalanced Data Classification Techniques, Anomaly Detection Techniques and Applications, Text and Document Classification Technologies
Другие ссылки: Journal of Artificial Intelligence Research (PDF)
Journal of Artificial Intelligence Research (HTML)
arXiv (Cornell University) (PDF)
arXiv (Cornell University) (HTML)
DataCite API (HTML)