计算机与数字工程2023，Vol.51Issue(10)：2390-2394,5.DOI:10.3969/j.issn.1672-9722.2023.10.033
一种基于半监督集成学习的软件缺陷预测方法
A Software Defect Prediction Method Based on Semi Supervised Ensemble Learning
摘要
Abstract
Software defect prediction is an effective way to improve software quality.In order to solve the problems of unbal-anced distribution and feature redundancy of software defect data,an improved software defect prediction method SSFSAdaBoost(semi supervised software defect prediction based on sampling,feature selection and AdaBoost)based on semi supervised ensem-ble learning is proposed.Firstly,the training set is mixed sampled,then the SMA optimization algorithm is used to select the fea-tures of the sampled training set and test set,and finally the improved semi supervised algorithm SUDAdaBoost is used for integra-tion.Experiments are carried out on three public data sets.The experimental results show that this method is superior to the initial AdaBoost algorithm,and has good performance in alleviating class imbalance problems.关键词
软件缺陷预测/半监督学习/集成学习/数据采样/特征选择Key words
software defect prediction/semi supervised learning/ensenmble learning/data sampling/feature selection分类
信息技术与安全科学引用本文复制引用
张莹,朱丽娜..一种基于半监督集成学习的软件缺陷预测方法[J].计算机与数字工程,2023,51(10):2390-2394,5.基金项目
国家自然科学基金项目(编号:61562004,71862003)资助. （编号:61562004,71862003）
