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Can data transformation help in the detection of fault-prone modules

By Yue Jiang, Bojan Cukic and Tim Menzies

Abstract

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 transformations differs. Random forest algorithm, for example, performs better with original and log transformed data set. Boosting and NaiveBayes perform significantly better with discretized data. We conclude that no general benefit can be expected from data transformations. Instead, selected transformation techniques are recommended to boost the performance of specific classification algorithms. 1

Publisher: ACM
Year: 2008
OAI identifier: oai:CiteSeerX.psu:10.1.1.352.8435
Provided by: CiteSeerX
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