WANG Yu-hong,FAN Jing,LEI Min,et al.Software Defect Prediction Model based on NPE-SVM[J].Journal of Chengdu University of Information Technology,2018,(03):286-289.[doi:10.16836/j.cnki.jcuit.2018.03.011]
基于NPE-SVM的软件缺陷预测模型
- Title:
- Software Defect Prediction Model based on NPE-SVM
- 文章编号:
- 2096-1618(2018)03-0286-04
- Keywords:
- software defects; neighborhood preserving embedding; machine learning; pattern recognition; manifold learning
- 分类号:
- TP311.53
- 文献标志码:
- A
- 摘要:
- 针对软件缺陷预测中数据集的类不均衡、高维、小采样以及非线性降维问题,提出基于领域保持嵌入支持向量机的软件缺陷预测模型。模型采用NPE算法对数据集进行降维处理,通过将NPE算法中奇异的广义特征计算转化为两个特征分解问题,得到了更准确的稳健解,有效规避了属性约减后导致的预测精度下降问题。选用支持向量机作为基础分类器,仿真实验结果表明,与其他方法相比,预测模型的查全率及F-measure值指标显著提高了2%～4%。
- Abstract:
- Aiming at the problem of data set imbalance, high dimension, small sampling and nonlinear dimensionality reduction in software defect prediction, this paper proposes a software defect prediction model based on Neighborhood preserving embedding support vector machine.The model uses NPE algorithm to reduce the dimension of the dataset. By transforming the singular generalized feature in the NPE algorithm into two feature decomposition problems, get a more accurate and robust solution, which effectively avoids the prediction caused by the attribute reduction decrease in accuracy. The support vector machine is chosen as the basic classifier. The simulation results show that compared with other methods, the recalling rate and F-measure index of the evaluation model are significantly increased by 2% ～4%.
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备注/Memo
收稿日期:2018-03-20基金项目:国家自然科学基金资助项目(61540063); 云南省教育厅资助项目(2017ZDX045); 贵州省公共大数据重点实验室开放课题基金资助项目(2017BDKFJJ017)