- (Chawla et al., 2002) ⇒ Nitesh V. Chawla, Kevin W. Bowyer, Lawrence O. Hall, and W. Philip Kegelmeyer. (2002). “SMOTE: Synthetic Minority over-sampling Technique.” In: Journal of Artificial Intelligence Research, 16(1).
Subject Headings: SMOTE Algorithm, Imbalanced Data Supervised Classification Algorithm.
- (He & Garcia, 2009) ⇒ Haibo He, and Edwardo A. Garcia. (2009). “Learning from Imbalanced Data.” In: Knowledge and Data Engineering, IEEE Transactions, 21(9).
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.
|2002 SMOTESyntheticMinorityOverSampl||Nitesh V. Chawla|
Kevin W. Bowyer
Lawrence O. Hall
W. Philip Kegelmeyer
|SMOTE: Synthetic Minority over-sampling Technique||2002|