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首页> 外文会议>2018 IEEE Intl Conf on Parallel amp; Distributed Processing with Applications, Ubiquitous Computing amp; Communications, Big Data amp; Cloud Computing, Social Computing amp; Networking, Sustainable Computing amp; Communications >Adaptive Centre-Weighted Oversampling for Class Imbalance in Software Defect Prediction
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Adaptive Centre-Weighted Oversampling for Class Imbalance in Software Defect Prediction

机译：用于软件缺陷预测中类别不平衡的自适应中心加权过采样

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摘要

In the field of software engineering, software defect prediction can maintain the high quality of software products, which is a popular current research topic. However, class imbalance affects the overall classification accuracy of software defect prediction models which is the key issue to be resolved. A new method called adaptive centre-weighted oversampling (ACWO) is proposed to effectively address imbalanced learning problems. First, an appropriate neighborhood size and neighbors are determined for each minority class sample. Then, for a minority class sample, the adaptive centre that is within its neighborhood range, its neighbors and the minority class sample are used to generate synthetic samples. Finally, oversampling of each minority class sample is carried out based on the weights assigned to them. These weights are obtained according to the neighborhood sizes and Euclidean distances to the centre. Afterwards, the software defect prediction model is eventually established by ACWO algorithm with stacked denoising auto-encoder neural network. Experimental results show that the software defect prediction model based on ACWO algorithm has a better performance than based on many existing class imbalance learning algorithms according to the precision P, recall R, F1 measure, G-mean, and AUC values.
机译：在软件工程领域，软件缺陷预测可以保持软件产品的高质量，这是当前流行的研究课题。但是，类不平衡会影响软件缺陷预测模型的总体分类精度，这是需要解决的关键问题。提出了一种称为自适应中心加权过采样（ACWO）的新方法，以有效解决不平衡的学习问题。首先，为每个少数族裔样本确定合适的邻域大小和邻居。然后，对于少数族裔样本，使用在其邻域范围内的自适应中心，其邻居和少数族裔样本生成合成样本。最后，根据分配给它们的权重对每个少数族裔样本进行超采样。这些权重是根据邻域大小和到中心的欧几里得距离得出的。然后，通过带有堆叠降噪自动编码器神经网络的ACWO算法最终建立了软件缺陷预测模型。实验结果表明，基于ACWO算法的软件缺陷预测模型在精度P，召回率R，F1度量，G均值和AUC值方面，比基于许多现有类不平衡学习算法的性能更好。

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