典型文献
WCM-WTrA:A Cross-Project Defect Predic-tion Method Based on Feature Selection and Distance-Weight Transfer Learning
文献摘要：
Cross-project defect prediction is a hot topic in the field of defect prediction.How to reduce the difference between projects and make the model have bet-ter accuracy is the core problem.This paper starts from two perspectives:feature selection and distance-weight in-stance transfer.We reduce the differences between projects from the perspective of feature engineering and introduce the transfer learning technology to construct a cross-project defect prediction model WCM-WTrA and multi-source model Multi-WCM-WTrA.We have tested on AEEEM and ReLink datasets,and the results show that our method has an average improvement of 23％compared with TCA+algorithm on AEEEM datasets,and an average improvement of 5％on ReLink datasets.
文献关键词：
中图分类号：
作者姓名：
LEI Tianwei;XUE Jingfeng;WANG Yong;NIU Zequn;SHI Zhiwei;ZHANG Yu
作者机构：
School of Computer Science and Technology,Beijing Institute of Technology,Beijing 100081,China;China Information Technology Security Evaluation Center,Beijing 100085,China;School of Electrical and Information Engineering,Beijing University of Civil Engineering and Architecture,Beijing 100044,China
文献出处：
引用格式：
[1]LEI Tianwei;XUE Jingfeng;WANG Yong;NIU Zequn;SHI Zhiwei;ZHANG Yu-.WCM-WTrA:A Cross-Project Defect Predic-tion Method Based on Feature Selection and Distance-Weight Transfer Learning)[J].电子学报（英文）,2022(02):354-366
A类：
WTrA,AEEEM,ReLink,TCA+algorithm
B类：
WCM,Cross,Project,Defect,Predic,Method,Based,Feature,Selection,Distance,Weight,Transfer,Learning,defect,prediction,hot,topic,field,How,reduce,between,projects,make,model,have,ter,accuracy,core,problem,This,paper,starts,from,two,perspectives,feature,selection,distance,weight,transfer,differences,engineering,introduce,learning,technology,construct,cross,multi,source,Multi,tested,datasets,results,show,that,method,has,average,improvement,compared
AB值：
0.477673
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