# Identify High-Impact Bug Reports by Combining the Data Reduction and Imbalanced Learning Strategies
## 5 Experimental Results
### RQ1:What Are the Better Classification Results for the Reduced Datasets and the Unreduced Datasets?
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Datasets** & **Evaluation Metrics** & **Origin** & **GA(FS+IS)** & **GA(FS\_IS)** & **GA(IS\_FS)** & **FS** & **IS** \\ \hline \multirow{8}{*}{Mozilla} & Accuracy & 0.7565 & 0.7107 & 0.7505 & 0.7357 & 0.7681 & **0.8026** \\  & Fmeasure & 0.755 & 0.7106 & 0.7506 & 0.7355 & 0.7689 & **0.803** \\  & Precision & 0.7969 & 0.7135 & 0.7526 & 0.7627 & 0.7847 & **0.8238** \\  & Recall & 0.7565 & 0.7107 & 0.7505 & 0.7357 & 0.7681 & **0.8026** \\  & AUC & 0.8249 & 0.7659 & 0.8095 & 0.8125 & 0.851 & **0.8545** \\ \hline \multirow{8}{*}{GCC} & Accuracy & 0.7582 & 0.785 & **0.796** & 0.7887 & 0.7755 & 0.7782 \\  & Fmeasure & 0.7719 & 0.7885 & **0.796** & 0.7905 & 0.7796 & 0.79 \\ \cline{1-1}  & Precision & 0.7994 & 0.7926 & **0.796** & 0.7926 & 0.7846 & 0.8136 \\ \cline{1-1}  & Recall & 0.7582 & 0.785 & **0.796** & 0.7887 & 0.7755 & 0.7782 \\ \cline{1-1}  & AUC & 0.7769 & 0.7848 & **0.8149** & 0.8019 & 0.7928 & 0.7834 \\ \hline \multirow{8}{*}{Eclipse} & Accuracy & 0.5549 & **0.6907** & 0.5876 & 0.5942 & 0.6399 & 0.5427 \\  & Fmeasure & 0.5486 & **0.6983** & 0.5911 & 0.6011 & 0.6467 & 0.5323 \\ \cline{1-1}  & Precision & 0.7331 & **0.738** & 0.7282 & 0.7088 & 0.7348 & 0.7342 \\ \cline{1-1}  & Recall & 0.5549 & **0.6907** & 0.5876 & 0.5942 & 0.6399 & 0.5427 \\ \cline{1-1}  & AUC & 0.723 & **0.7778** & 0.7357 & 0.7242 & 0.7707 & 0.7202 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The effect of the feature selection and instance selection order on the experimental results for the Mozilla and GCC datasets when using NBM as a classifier. From Table 9, we find that the imbalance_GA(FS+IS) method (that is, first using the SMOTE method for unbalanced data sets and then using feature selection and instance selection at the same time) can achieve the maximum performance improvement for identifying high-impact bug reports for the Mozilla dataset. The accuracy, F-measure, precision, recall and AUC values are 0.7435, 0.7444, 0.7615, 0.7435, and 0.8322, respectively. For the GCC and Eclipse datasets, it can be found that the GA(FS+IS_imbalance method (that is, first using the feature selection and instance selection for the dataset, then using the ROS method for imbalance processing) performs better in identifying high-impact bug reports. For the GCC dataset, the accuracy, the F-measure, the precision, the recall, and AUC values are 0.7562, 0.7725 0.805, 0.7562, and 0.7853, respectively. For the Eclipse dataset, the accuracy, F-measure, and recall values are 0.6251, 0.6303, 0.7207, 0.6251, and 0.7761, respectively. From Table 10, we can observe that the imbalance_GA(FS) method (that is, using the SMOTE method to imbalance the dataset first, and then only using feature selection) has the greatest improvement for identifying high-impact bug reports for the Mozilla dataset. The accuracy, F-measure, precision, recall and AUC values are 0.762, 0.7616, 0.7926, 0.762, and 0846, respectively. For the GCC dataset, the GA(FS)_imbalance method (that is, the dataset is processed by feature selection first, and then the dataset is processed using the CMA method) performs better in identifying high-impact bug reports. The accuracy, F-measure, precision, recall and AUC values are 0.7778, 0.7878, 0.8054, 0.7778, and 0.799, respectively. For the Eclipse dataset, the GA(FS)_imbalance method (that is, feature selection is used first, and then the ROS method is used to imbalance the dataset) performs better in identifying high-impact bug reports. The accuracy, F-measure, precision, recall and AUC values are 0.5697, 0.5686, 0.7319, 0.5697, and 0.734, respectively. From Table 11, For the Mozilla and GCC datasets, the GA(IS)_imbalance method (that is, dataset is processed by feature selection first and then the CMA method is used to imbalance the data set) performs better in identifying high-impact bug reports. For the Mozilla dataset, the accuracy, the F-measure, the precision, the recall, and AUC values are 0.8044, 0.8048, 0.8251, 0.8044, and 0.8547, respectively.