计算机应用研究2013，Vol.30Issue(9)：2748-2751,2754,5.DOI:10.3969/j.issn.1001-3695.2013.09.047
基于LASSO-SVM的软件缺陷预测模型研究
Research of software defect prediction model based on LASSO-SVM
摘要
Abstract
The prediction accuracy of most current software defect prediction models is not very high.To solve the problem,this paper investigated a software defect prediction model with the least absolute shrinkage and select operator(LASSO) and the support vector machine (SVM).At first,it reduced the dimension of the original data sets and extracted the data which was irrelevant with software defect prediction by taking advantage of the feature selection capability of LASSO.Then it found the correlated optimal weights of the SVM by making use of the parameter preference capability of cross validation.At last,it finished software defect prediction by utilizing the non-linear computing power of SVM.The simulation experiment indicated that proposed method owe a higher prediction precision than the traditional ones and predicted faster.关键词
软件缺陷预测/最小绝对值压缩与选择方法/特征选择/支持向量机/交叉验证Key words
software defect prediction/ least absolute shrinkage and select operator (LASSO)/ feature selection/ support vector machine (SVM) / cross validation分类
信息技术与安全科学引用本文复制引用
吴晓萍,赵学靖,乔辉,刘东梅,王志..基于LASSO-SVM的软件缺陷预测模型研究[J].计算机应用研究,2013,30(9):2748-2751,2754,5.基金项目
国家教育部留学回国人员科研启动基金资助项目(第44批) （第44批）
兰州大学中央高校基本科研业务费专项资金资助项目(lzujbky-2012-15,lzujbky-2013-178) （lzujbky-2012-15,lzujbky-2013-178）
