Comparison of Outlier Detection Methods in Fault-Prone Module Detection Models
نویسندگان
چکیده
In this paper, we experimentally evaluate outlier detection methods, which detect data points that are far away from others in a data set, in terms of improving the prediction performance of fault-prone module detection models. In the experiment, we compared two outlier detection methods (MOA, LOFM) each applied to three wellknown fault-prone module detection models (LDA, LRA, CT). The result showed that MOA improved F1-values of all fault-proneness 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).
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