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Can data transformation help in the detection of fault-prone modules?
2008
Proceedings of the 2008 workshop on Defects in large software systems - DEFECTS '08
Data preprocessing (transformation) plays an important role in data mining and machine learning. In this study, we investigate the effect of four different preprocessing methods to fault-proneness prediction using nine datasets from NASA Metrics Data Programs (MDP) and ten classification algorithms. Our experiments indicate that log transformation rarely improves classification performance, but discretization affects the performance of many different algorithms. The impact of different
doi:10.1145/1390817.1390822
dblp:conf/issta/JiangCM08
fatcat:edbyqgno75a3jd42k3uiktwxy4