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- W89025900 abstract "†† † † † To improve the prediction performance of fault-proneness models, this paper experimentally evaluates the effect of over and under sampling methods, which are preprocessing procedures for a fit dataset. The sampling methods are expected to improve the prediction performance when the fit dataset is imbalanced, i.e. there exists a large bias between the number of faultprone modules and the number of non-fault-prone modules. There is, however, no research that reports the effects of applying sampling methods to fault-proneness models. In the experiment, we evaluated the effects of four sampling methods (ROS, SMOTE, RUS, TLUS) applied to four fault-proneness models (linear discriminant analysis, logistic regression analysis, neural network and classification tree) by using two module sets (ratios of fault-prone modules are 5.67% and 13.16%) derived from large-scale legacy software that has been developed and maintained by a Japanese software company. The results showed that all four sampling methods improved the prediction performance of every fault-proneness model except the classification tree. However, the improvement by TLUS was less than that of the other sampling methods. The improvements in F1-Values were 0.079 at minimum, 0.303 at maximum and 0.146 at the mean when three sampling methods (ROS, SMOTE and RUS) were applied to three fault-proneness models (linear, logistic and neural network)." @default.
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- W89025900 date "2007-01-01" @default.
- W89025900 modified "2023-09-24" @default.
- W89025900 title "Applying Sampling Methods to Fault-prone Module Detection" @default.
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