# 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 c} \hline \hline \multirow{2}{*}{**Datasets**} & \multirow{2}{*}{**Evaluation Metrics**} & \multicolumn{4}{c}{**GA(FS)\_Imbalance**} & \multicolumn{4}{c}{**Imbalance\_GA(FS)**} \\ \cline{3-9}  & & **RUS** & **ROS** & **CMA** & **SMOTE** & **RUS** & **ROS** & **SMOTE** \\ \hline \multirow{9}{*}{Mozilla} & Accuracy & 0.7444 & 0.7444 & 0.7404 & 0.7404 & 0.7505 & 0.7597 & **0.762** \\  & Fmeasure & 0.7428 & 0.7438 & 0.7392 & 0.7397 & 0.7503 & 0.7593 & **0.7616** \\  & Precision & 0.7594 & 0.7531 & 0.7523 & 0.7489 & 0.7781 & 0.7912 & **0.7926** \\  & Recall & 0.7444 & 0.7444 & 0.7404 & 0.7404 & 0.7505 & 0.7597 & **0.762** \\  & AUC & 0.8139 & 0.8113 & 0.809 & 0.8098 & 0.834 & 0.831 & **0.846** \\ \hline \multirow{9}{*}{GCC} & Accuracy & 0.7541 & 0.7741 & **0.7778** & 0.776 & 0.74 & 0.7455 & 0.7273 \\  & Fmeasure & 0.7686 & 0.7843 & **0.7878** & 0.7878 & 0.7583 & 0.7608 & 0.7466 \\ \cline{1-1}  & Precision & 0.7978 & 0.802 & **0.8054** & 0.811 & 0.8027 & 0.7921 & 0.7921 \\ \cline{1-1}  & Recall & 0.7541 & 0.7741 & **0.7778** & 0.776 & 0.74 & 0.7455 & 0.7273 \\ \cline{1-1}  & AUC & 0.7929 & 0.798 & **0.799** & 0.7959 & 0.7651 & 0.7843 & 0.7645 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The results of GA(FS)_imbalance and imbalance_GA(FS) on the Mozilla, GCC and Eclipse datasets. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & \multirow{2}{*}{**Evaluation Metrics**} & \multicolumn{4}{c}{**GA(FS+IS)\_Imbalance**} & \multicolumn{4}{c}{**Imbalance\_GA(FS+IS)**} \\ \cline{3-9}  & & **RUS** & **ROS** & **CMA** & **SMOTE** & **RUS** & **ROS** & **SMOTE** \\ \hline \multirow{9}{*}{Mozilla} & Accuracy & 0.6983 & 0.6963 & 0.7025 & 0.6921 & 0.7223 & 0.7327 & **0.7435** \\  & Fmeasure & 0.6965 & 0.6952 & 0.7017 & 0.6909 & 0.723 & 0.7326 & **0.7444** \\  & Precision & 0.7086 & 0.7033 & 0.7087 & 0.6997 & 0.7462 & 0.7618 & **0.7615** \\  & Recall & 0.6983 & 0.6963 & 0.7025 & 0.6921 & 0.7223 & 0.7327 & **0.7435** \\  & AUC & 0.7701 & 0.771 & 0.7665 & 0.7679 & 0.8056 & 0.8164 & **0.8322** \\ \hline \multirow{9}{*}{GCC} & Accuracy & 0.7102 & **0.7562** & 0.7543 & 0.7466 & 0.7286 & 0.7091 & 0.7164 \\  & Fmeasure & 0.7348 & **0.7725** & 0.7705 & 0.7648 & 0.7476 & 0.7313 & 0.739 \\ \cline{1-1}  & Precision & 0.7941 & **0.805** & 0.8022 & 0.8031 & 0.7909 & 0.7871 & 0.8041 \\ \cline{1-1}  & Recall & 0.7102 & **0.7562** & 0.7543 & 0.7466 & 0.7286 & 0.7091 & 0.7164 \\ \cline{1-1}  & AUC & 0.7734 & **0.7853** & 0.786 & 0.7855 & 0.7805 & 0.7651 & 0.7691 \\ \hline \multirow{9}{*}{Eclipse} & Accuracy & 0.6136 & **0.6251** & 0.6218 & 0.621 & 0.5114 & 0.4859 & 0.4894 \\ \cline{1-1}  & Fmeasure & 0.6162 & **0.6303** & 0.6263 & 0.6259 & 0.4884 & 0.4541 & 0.4602 \\ \cline{1-1}  & Precision & 0.7272 & **0.7207** & 0.7229 & 0.7186 & 0.7375 & 0.7237 & 0.7204 \\ \cline{1-1}  & Recall & 0.6136 & **0.6251** & 0.6218 & 0.621 & 0.5114 & 0.4859 & 0.4894 \\ \cline{1-1}  & AUC & 0.771 & **0.7761** & 0.7764 & 0.7746 & 0.6762 & 0.6842 & 0.6728 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The results of GA(FS+IS)_imbalance and imbalance_GA(FS+IS) on the Mozilla, GCC and Eclipse datasets.