Comparison of outlier detection methods in fault-proneness models

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

doi:10.1109/ESEM.2007.34

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 proceeding › Conference contribution

9 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 language	English
Title of host publication	Proceedings - 1st International Symposium on Empirical Software Engineering and Measurement, ESEM 2007
Pages	461-463
Number of pages	3
DOIs	https://doi.org/10.1109/ESEM.2007.34
Publication status	Published - 2007
Externally published	Yes
Event	1st International Symposium on Empirical Software Engineering and Measurement, ESEM 2007 - Madrid, Spain Duration: Sept 20 2007 → Sept 21 2007

Publication series

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

Other

Other	1st International Symposium on Empirical Software Engineering and Measurement, ESEM 2007
Country/Territory	Spain
City	Madrid
Period	9/20/07 → 9/21/07

All Science Journal Classification (ASJC) codes

Computer Science(all)
Software

Access to Document

10.1109/ESEM.2007.34

Cite this

Matsumoto, S., Kamei, Y., Monden, A., & Matsumoto, K. I. (2007). Comparison of outlier detection methods in fault-proneness models. In Proceedings - 1st International Symposium on Empirical Software Engineering and Measurement, ESEM 2007 (pp. 461-463). Article 4343779 (Proceedings - 1st International Symposium on Empirical Software Engineering and Measurement, ESEM 2007). https://doi.org/10.1109/ESEM.2007.34

Comparison of outlier detection methods in fault-proneness models. / Matsumoto, Shinsuke; Kamei, Yasutaka; Monden, Akito et al.
Proceedings - 1st International Symposium on Empirical Software Engineering and Measurement, ESEM 2007. 2007. p. 461-463 4343779 (Proceedings - 1st International Symposium on Empirical Software Engineering and Measurement, ESEM 2007).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Matsumoto, S, Kamei, Y, Monden, A & Matsumoto, KI 2007, Comparison of outlier detection methods in fault-proneness models. in Proceedings - 1st International Symposium on Empirical Software Engineering and Measurement, ESEM 2007., 4343779, Proceedings - 1st International Symposium on Empirical Software Engineering and Measurement, ESEM 2007, pp. 461-463, 1st International Symposium on Empirical Software Engineering and Measurement, ESEM 2007, Madrid, Spain, 9/20/07. https://doi.org/10.1109/ESEM.2007.34

Matsumoto S, Kamei Y, Monden A, Matsumoto KI. Comparison of outlier detection methods in fault-proneness models. In Proceedings - 1st International Symposium on Empirical Software Engineering and Measurement, ESEM 2007. 2007. p. 461-463. 4343779. (Proceedings - 1st International Symposium on Empirical Software Engineering and Measurement, ESEM 2007). doi: 10.1109/ESEM.2007.34

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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).",

author = "Shinsuke Matsumoto and Yasutaka Kamei and Akito Monden and Matsumoto, {Ken Ichi}",

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Comparison of outlier detection methods in fault-proneness models

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