SMOTE: Synthetic minority over-sampling technique
SMOTE: Synthetic minority over-sampling technique
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DOI：
10.1613/jair.953
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发表时间：
2002-01-01
影响因子：
5
通讯作者：
Kegelmeyer, WP
中科院分区：
文献类型：
--
作者：
Chawla, NV;Bowyer, KW;Kegelmeyer, WP
描述了一种从不平衡数据集构造分类器的方法。如果分类类别没有近似相等地表示，则数据集是不平衡的。通常，真实世界的数据集主要由正常示例组成，只有一小部分异常或有趣的示例。同样的情况是，将一个异常（感兴趣的）示例错误分类为正常示例的成本通常比反向错误的成本高得多。下采样的多数（正常）类已被提出作为一个很好的手段，增加敏感性的分类少数类。本文表明，我们对少数（异常）类进行过采样和对多数（正常）类进行欠采样的方法相结合，可以比仅对多数类进行欠采样获得更好的分类器性能（在ROC空间中）。本文还表明，我们的过采样少数类和欠采样多数类的方法相结合，可以实现更好的分类器性能（在ROC空间）比改变Ripper或类先验的损失率在朴素贝叶斯。我们对少数类进行过采样的方法包括创建合成少数类示例。使用C4.5，Ripper和朴素贝叶斯分类器进行实验。使用受试者工作特征曲线（AUC）下的面积和ROC凸船体策略评估该方法。
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 sensitvity of a classier 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.