云南民族大学学报（自然科学版）2019，Vol.28Issue(1)：77-80,4.DOI:10.3969/j.issn.1672-8513.2019.01.016
基于线性LTSA算法维数约减的软件缺陷预测研究
A software defect prediction model based on the LTSA dimension reduction algorithm
摘要
Abstract
Software defect prediction is an important technology in the software development process. For a high-dimensional and small sampling of software defect data sets, the prediction accuracy is degraded. A linear partial-cutting spatial arrangement algorithm is used to reduce the dimensionality of data sets. The vector machine is used as the basic classifier for binary classification, and the software defect prediction model is established. The prediction accuracy of the model is evaluated by the two-dimensional confusion matrix. The experimental results show that compared with other models, this model can be reduced to a lower dimension with fewer neighborhood points, without re-learning the popular geometry of the sample space, directly mapping new sample points, and predicting the time cost. From 13. 7269 seconds to 6. 2178 seconds, the time cost for parameter optimization is reduced from267. 4421 seconds to 165. 98 seconds, effectively improving the efficiency of software defect prediction.关键词
软件缺陷预测/线性LTSA算法/流形学习/支持向量机Key words
software defect prediction/linear LTSA algorithm/popular learning/support vector machine分类
信息技术与安全科学引用本文复制引用
王玉红,范菁,曲金帅,冯景义..基于线性LTSA算法维数约减的软件缺陷预测研究[J].云南民族大学学报（自然科学版）,2019,28(1):77-80,4.基金项目
国家自然科学基金(61540063) （61540063）
云南省应用基础研究计划项目(2018FD055) （2018FD055）
云南省教育厅科学研究基金(2017ZDX045) （2017ZDX045）
云南民族大学校级项目(2017QN02) （2017QN02）
"多元感知网及信息物理系统"科学创新团队开放式基金 （）
