SMOTE for high-dimensional class-imbalanced data.
SMOTE for high-dimensional class-imbalanced data.
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DOI：
10.1186/1471-2105-14-106
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发表时间：
2013-03-22
影响因子：
3
通讯作者：
Lusa L
中科院分区：
文献类型：
--
作者：
Blagus R;Lusa L
使用类别不平衡数据进行的分类偏向于多数类别。对于高维数据，偏差甚至更大，其中变量的数量大大超过样本的数量。这个问题可以通过欠采样或过采样来缓解，这会产生类平衡的数据。一般来说，欠采样是有帮助的，而随机过采样则没有。合成少数过采样技术（SMOTE）是一种非常流行的过采样方法，被提出来改进随机过采样，但其在高维数据上的行为尚未得到彻底研究。在本文中，我们使用模拟和真实的高维数据，从理论和实证的角度研究了 SMOTE 的特性。虽然在大多数情况下，SMOTE 似乎对低维数据有益，但当数据是高维时，它不会减弱大多数分类器在多数类中分类的偏差，并且它的效果不如随机欠采样。如果执行某种类型的变量选择时变量数量减少，SMOTE 对于高维数据的 k-NN 分类器是有益的；我们解释为什么 k-NN 分类偏向于少数类。此外，我们表明，在高维数据上，SMOTE 不会改变特定于类的平均值，同时会降低数据变异性并引入样本之间的相关性。我们解释了我们的发现如何影响高维数据的类别预测。实际上，在高维设置中，只有基于欧几里德距离的 k-NN 分类器似乎能从 SMOTE 的使用中受益匪浅，前提是在使用 SMOTE 之前执行变量选择；如果使用更多的邻居，好处就更大。不应该使用没有变量选择的 k-NN 的 SMOTE，因为它使分类强烈偏向于少数类。
Classification using class-imbalanced data is biased in favor of the majority class. The bias is even larger for high-dimensional data, where the number of variables greatly exceeds the number of samples. The problem can be attenuated by undersampling or oversampling, which produce class-balanced data. Generally undersampling is helpful, while random oversampling is not. Synthetic Minority Oversampling TEchnique (SMOTE) is a very popular oversampling method that was proposed to improve random oversampling but its behavior on high-dimensional data has not been thoroughly investigated. In this paper we investigate the properties of SMOTE from a theoretical and empirical point of view, using simulated and real high-dimensional data. While in most cases SMOTE seems beneficial with low-dimensional data, it does not attenuate the bias towards the classification in the majority class for most classifiers when data are high-dimensional, and it is less effective than random undersampling. SMOTE is beneficial for k-NN classifiers for high-dimensional data if the number of variables is reduced performing some type of variable selection; we explain why, otherwise, the k-NN classification is biased towards the minority class. Furthermore, we show that on high-dimensional data SMOTE does not change the class-specific mean values while it decreases the data variability and it introduces correlation between samples. We explain how our findings impact the class-prediction for high-dimensional data. In practice, in the high-dimensional setting only k-NN classifiers based on the Euclidean distance seem to benefit substantially from the use of SMOTE, provided that variable selection is performed before using SMOTE; the benefit is larger if more neighbors are used. SMOTE for k-NN without variable selection should not be used, because it strongly biases the classification towards the minority class.
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影响因子：
4.3
作者：
Liu, Yang;Chawla, Nitesh V.;Stolcke, Andreas
通讯作者：
Stolcke, Andreas
影响因子：
3
作者：
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Lusa L
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