{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/smote-synthetic-minority-over-sampling","title":"SMOTE: Synthetic Minority Over-sampling Technique","arxiv_id":"1106.1813","date":"2011-06-09","proceeding":null,"authors":["N. V. Chawla","K. W. Bowyer","L. O. Hall","W. P. Kegelmeyer"],"abstract":"An approach to the construction of classifiers from imbalanced datasets is\ndescribed. A dataset is imbalanced if the classification categories are not\napproximately equally represented. Often real-world data sets are predominately\ncomposed of \"normal\" examples with only a small percentage of \"abnormal\" or\n\"interesting\" examples. It is also the case that the cost of misclassifying an\nabnormal (interesting) example as a normal example is often much higher than\nthe cost of the reverse error. Under-sampling of the majority (normal) class\nhas been proposed as a good means of increasing the sensitivity of a classifier\nto the minority class. This paper shows that a combination of our method of\nover-sampling the minority (abnormal) class and under-sampling the majority\n(normal) class can achieve better classifier performance (in ROC space) than\nonly under-sampling the majority class. This paper also shows that a\ncombination of our method of over-sampling the minority class and\nunder-sampling the majority class can achieve better classifier performance (in\nROC space) than varying the loss ratios in Ripper or class priors in Naive\nBayes. Our method of over-sampling the minority class involves creating\nsynthetic minority class examples. Experiments are performed using C4.5, Ripper\nand a Naive Bayes classifier. The method is evaluated using the area under the\nReceiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.","url_abs":"http://arxiv.org/abs/1106.1813v1","url_pdf":"http://arxiv.org/pdf/1106.1813v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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