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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Classification of Imbalanced Data by Oversampling in Kernel Space of Support Vector Machines
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Classification of Imbalanced Data by Oversampling in Kernel Space of Support Vector Machines

机译：支持向量机核空间中过采样的不平衡数据分类。

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摘要

Historical data sets for fault stage diagnosis in industrial machines are often imbalanced and consist of multiple categories or classes. Learning discriminative models from such data sets is challenging due to the lack of representative data and the bias of traditional classifiers toward the majority class. Sampling methods like synthetic minority oversampling technique (SMOTE) have been traditionally used for such problems to artificially balance the data set before being trained by a classifier. This paper proposes a weighted kernel-based SMOTE (WK-SMOTE) that overcomes the limitation of SMOTE for nonlinear problems by oversampling in the feature space of support vector machine (SVM) classifier. The proposed oversampling algorithm along with a cost-sensitive SVM formulation is shown to improve performance when compared to other baseline methods on multiple benchmark imbalanced data sets. In addition, a hierarchical framework is developed for multiclass imbalanced problems that have a progressive class order. The proposed WK-SMOTE and hierarchical framework are validated on a real-world industrial fault detection problem to identify deterioration in insulation of high-voltage equipments.
机译：用于工业机械故障阶段诊断的历史数据集通常不平衡，并且包含多个类别或类别。由于缺乏代表性数据以及传统分类器偏向多数分类，因此从此类数据集中学习判别模型具有挑战性。传统上，诸如合成少数采样技术（SMOTE）之类的采样方法已用于解决此类问题，从而在分类器进行训练之前人为地平衡了数据集。本文提出了一种基于加权核的SMOTE（WK-SMOTE），它通过在支持向量机（SVM）分类器的特征空间中进行过采样来克服SMOTE对于非线性问题的局限性。与多个基准不平衡数据集上的其他基准线方法相比，建议的过采样算法以及对成本敏感的SVM公式可提高性能。此外，针对具有逐步类别顺序的多类别不平衡问题，开发了一个层次框架。提出的WK-SMOTE和分层框架已针对实际的工业故障检测问题进行了验证，以识别高压设备的绝缘性能是否下降。

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