Comparison of Outlier Detection Methods in Fault-proneness Models

  • Authors:

  • Shinsuke Matsumoto;Yasutaka Kamei;Akito Monden;Ken-ichi Matsumoto

  • Affiliations:

  • Nara Institute of Science and Technology, Japan;Nara Institute of Science and Technology, Japan;Nara Institute of Science and Technology, Japan;Nara Institute of Science and Technology, Japan

  • Venue:
  • ESEM '07 Proceedings of the First International Symposium on Empirical Software Engineering and Measurement
  • Year:
  • 2007

Quantified Score

Hi-index 0.00

Visualization

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).