Comparison of outlier detection methods in fault-proneness models

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

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

8 被引用数 (Scopus)

抄録

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).

本文言語英語
ホスト出版物のタイトルProceedings - 1st International Symposium on Empirical Software Engineering and Measurement, ESEM 2007
ページ461-463
ページ数3
DOI
出版ステータス出版済み - 2007
イベント1st International Symposium on Empirical Software Engineering and Measurement, ESEM 2007 - Madrid, スペイン
継続期間: 9 20 20079 21 2007

出版物シリーズ

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

その他

その他1st International Symposium on Empirical Software Engineering and Measurement, ESEM 2007
Countryスペイン
CityMadrid
Period9/20/079/21/07

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Software

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