Software quality modeling: The impact of class noise on the random forest classifier
Software quality modeling: The impact of class noise on the random forest classifier
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软件质量建模：类噪声对随机森林分类器的影响
DOI：
10.1109/cec.2008.4631321
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
Lofton A. Bullard
中科院分区：
文献类型：
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作者：
A. Folleco;T. Khoshgoftaar;J. V. Hulse;Lofton A. Bullard
这项研究调查了模拟类噪声水平不断提高对软件质量分类的影响。将类噪声注入了七个软件工程测量数据集中，分析了三个学习者，随机森林，C4.5和天真的贝叶斯的表现。随机森林分类器被用于本研究，因为它相对于众所周知和常用的分类器（例如C4.5和Naive贝叶斯）的性能很强。此外，相对较少的软件质量分类研究已经考虑了随机的森林分类器。在这项研究中考虑的实验因素是类噪声水平和注入噪声的少数族裔实例的百分比。经验结果表明，随机森林在所有实验中均获得了最佳，最一致的分类性能。
This study investigates the impact of increasing levels of simulated class noise on software quality classification. Class noise was injected into seven software engineering measurement datasets, and the performance of three learners, random forests, C4.5, and Naive Bayes, was analyzed. The random forest classifier was utilized for this study because of its strong performance relative to well-known and commonly-used classifiers such as C4.5 and Naive Bayes. Further, relatively little prior research in software quality classification has considered the random forest classifier. The experimental factors considered in this study were the level of class noise and the percent of minority instances injected with noise. The empirical results demonstrate that the random forest obtained the best and most consistent classification performance in all experiments.