MWMOTE-Majority Weighted Minority Oversampling Technique for Imbalanced Data Set Learning
MWMOTE-Majority Weighted Minority Oversampling Technique for Imbalanced Data Set Learning
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DOI：
10.1109/tkde.2012.232
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发表时间：
2014-02-01
影响因子：
8.9
通讯作者：
Murase, Kazuyuki
中科院分区：
文献类型：
--
作者：
Barua, Sukarna;Islam, Md. Monirul;Murase, Kazuyuki
不平衡学习问题包含了数据样本在不同类别之间的不均匀分布，对任何分类器都构成了挑战，因为少数类别样本的学习变得困难。合成过采样方法通过生成合成少数类样本来平衡多数类和少数类样本之间的分布，从而解决了这一问题。本文指出，现有的大多数过采样方法在某些情况下可能会产生错误的合成少数样本，从而使学习任务变得更加困难。为此，提出了一种新的方法，称为多数加权少数过采样技术(MWMOTE)，以有效地处理不平衡学习问题。MWMOTE首先识别难学的信息丰富的少数类样本，并根据它们与最近的多数类样本之间的欧几里得距离为它们分配权重。然后，它使用聚类方法从加权的信息量较大的少数类样本生成合成样本。这是以这样一种方式完成的，即所有生成的样本都位于某个少数类集群中。MWMOTE已经在4个人工数据集和20个真实世界数据集上进行了广泛的评估。仿真结果表明，该方法在几何均值(G-Mean)和接收端工作曲线下面积(ROC，通常称为曲线下面积，AUC)等评价指标上均优于或可与其他一些方法相媲美。
Imbalanced learning problems contain an unequal distribution of data samples among different classes and pose a challenge to any classifier as it becomes hard to learn the minority class samples. Synthetic oversampling methods address this problem by generating the synthetic minority class samples to balance the distribution between the samples of the majority and minority classes. This paper identifies that most of the existing oversampling methods may generate the wrong synthetic minority samples in some scenarios and make learning tasks harder. To this end, a new method, called Majority Weighted Minority Oversampling TEchnique (MWMOTE), is presented for efficiently handling imbalanced learning problems. MWMOTE first identifies the hard-to-learn informative minority class samples and assigns them weights according to their euclidean distance from the nearest majority class samples. It then generates the synthetic samples from the weighted informative minority class samples using a clustering approach. This is done in such a way that all the generated samples lie inside some minority class cluster. MWMOTE has been evaluated extensively on four artificial and 20 real-world data sets. The simulation results show that our method is better than or comparable with some other existing methods in terms of various assessment metrics, such as geometric mean (G-mean) and area under the receiver operating curve (ROC), usually known as area under curve (AUC).