Comparison of outlier detection methods in fault-proneness models

Shinsuke Matsumoto, Yasutaka Kamei, Akito Monden, Ken Ichi Matsumoto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

In this paper, we experimentally evaluated the effect of outlier detection methods to improve the prediction performance of fault-proneness models. Detected outliers were removed from a fit dataset before building a model. In the experiment, we compared three outlier detection methods (Mahalanobis outlier analysis (MOA), local outlier factor method (LOFM) and rule based modeling (RBM)) each applied to three well-known fault-proneness models (linear discriminant analysis (LDA), logistic regression analysis (LRA) and classification tree (CT)). As a result, MOA and RBM improved F1-values of all models (0.04 at minimum, 0.17 at maximum and 0.10 at mean) while improvements by LOFM were relatively small (-0.01 at minimum, 0.04 at maximum and 0.01 at mean).

Original languageEnglish
Title of host publicationProceedings - 1st International Symposium on Empirical Software Engineering and Measurement, ESEM 2007
Pages461-463
Number of pages3
DOIs
Publication statusPublished - Dec 1 2007
Externally publishedYes
Event1st International Symposium on Empirical Software Engineering and Measurement, ESEM 2007 - Madrid, Spain
Duration: Sep 20 2007Sep 21 2007

Publication series

NameProceedings - 1st International Symposium on Empirical Software Engineering and Measurement, ESEM 2007

Other

Other1st International Symposium on Empirical Software Engineering and Measurement, ESEM 2007
CountrySpain
CityMadrid
Period9/20/079/21/07

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Software

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