面向软件缺陷预测的过采样方法

为了缓解软件缺陷预测的类不平衡问题,避免过拟合影响缺陷预测模型的准确率,本文提出一种面向软件缺陷预测的基于异类距离排名的过采样方法(HDR).首先,对少数类实例进行3类实例区分,去除噪声实例,减少噪声数据导致的过拟合的情况,然后基于异类距离将实例进行排名,选取相似度高的实例两两组合产生新实例,以此来提升新实例的多样性,...

Oversampling Method for Software Defect Prediction

To alleviate the class imbalance problem of software defect prediction and avoid the influence of overfitting on the accuracy of the defect prediction model, this paper proposes an oversampling method for software defect prediction based on heterogeneous distance ranking (HDR). First, a minority of instances are distinguished by three classes to remove noise instances and reduce overfitting caused by noise data. Then, instances are ranked based on heterogeneous distances and paired with highly similar ones to generate new instances for the improvement of new instance diversity. Valuable minority instances that were deleted are restored afterward. The experiment compares the HDR algorithm with the SMOTE and the Borderline-SMOTE algorithms, and the RF classifier is used on the eight actual project data sets of NASA. The results show that there are 7.7% and 10.6% performance improvements on the F1-measure and G-Mean indicators respectively. Experimental results show that the HDR algorithm is significantly better than other algorithms in processing software defect prediction data sets with large data volumes and high imbalance rates.