基于DBSCAN算法的欠采样不平衡数据分类技术

Behzad Mirzaei, Bahareh Nikpour, H. Nezamabadi-pour
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引用次数: 3

摘要

在分类问题中,分类精度会受到训练数据的显著影响。然而,在实际应用中,数据集的分布大多是不平衡的。不平衡数据集意味着大多数样本都在一个被称为多数类的类别中,而另一个被称为少数类的类别样本很少。在这些情况下,大多数分类器都会遇到这个问题,因为它们的设计目的是对均匀分布在不同类别之间的样本进行分类。因此,在不平衡数据分类领域中,选择合适的训练集是必不可少的一步。本文提出了一种新颖有效的欠采样技术,利用著名的DBSCAN算法选择多数类的合适样本。根据该算法,从多数类中选择最合适的样本,并将其他多数类样本去除以平衡训练集。在15个不平衡数据集上的实验结果表明,与其他六种预处理方法相比,该方法具有优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An under-sampling technique for imbalanced data classification based on DBSCAN algorithm
In the classification problem, the classification accuracy will be influenced by the training data significantly. However, data sets distribution in real-world applications, is mostly imbalanced. Imbalanced data sets mean that most of the samples are in one class named the majority class, whereas the other class named the minority class has little samples. In these situations, most of the classifiers confront the problem, because they designed to classify samples that are distributed between classes equally. Therefore, selecting a suitable training set is an essential step in the domain of imbalanced data classification. In this paper, a novel and effective under-sampling technique is presented to select the suitable samples of majority class using the well-known DBSCAN algorithm. According to this algorithm, the most appropriate samples from the majority class are selected, and other majority class samples will be removed to balance the training set. Experimental results over fifteen imbalanced data sets demonstrate the supremacy of the proposed method compared with six other preprocessing methods.
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