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首页> 外文期刊>International journal of knowledge engineering and soft data paradigms >Borderline over-sampling for imbalanced data classification
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Borderline over-sampling for imbalanced data classification

机译：边界过采样以实现不平衡的数据分类

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摘要

Traditional classification algorithms usually provide poor accuracy on the prediction of the minority class of imbalanced data sets. This paper proposes a new method for dealing with imbalanced data sets by over-sampling the borderline minority class instances. A Support Vector Machine (SVM) classifier is then trained to predict future instances. Compared with other over-sampling methods, the proposed method focuses only on the minority class instances residing along the decision boundary, due to the fact that this region is the most crucial for establishing the decision boundary. Furthermore, the artificial minority instances are generated in such a way that the regions of the minority class with fewer majority class instances would be expanded by extrapolation, otherwise the current boundary of the minority class would be consolidated by interpolation. Experimental results show that the proposed method achieves a better performance than other over-sampling methods.
机译：传统的分类算法通常在预测不平衡数据集的少数类别时准确性较差。本文提出了一种通过对边缘少数类实例进行过度采样来处理不平衡数据集的新方法。然后，训练支持向量机（SVM）分类器来预测将来的实例。与其他过采样方法相比，由于该区域对于建立决策边界最关键，因此该方法仅关注决策边界上的少数类实例。此外，以这样的方式生成人工少数实例：通过外推扩展少数类实例较少的少数类区域，否则将通过插值合并少数类的当前边界。实验结果表明，该方法具有比其他过采样方法更好的性能。

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