# 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(IS\_FS)\_imbalance**} & \multicolumn{4}{c}{**imbalance\_GA(IS\_FS)**} \\ \cline{3-9}  & & **RUS** & **ROS** & **CMA** & **SMOTE** & **RUS** & **ROS** & **SMOTE** \\ \hline \multirow{9}{*}{Mozilla} & Accuracy & 0.7431 & 0.7357 & 0.732 & 0.7264 & 0.7188 & 0.7724 & **0.7532** \\  & Fmeasure & 0.7431 & 0.7354 & 0.7317 & 0.7258 & 0.7201 & 0.7734 & **0.7544** \\  & Precision & 0.7691 & 0.764 & 0.7602 & 0.7577 & 0.725 & 0.7889 & **0.7642** \\  & Recall & 0.7431 & 0.7357 & 0.732 & 0.7264 & 0.7188 & 0.7724 & **0.7532** \\  & AUC & 0.8128 & 0.8108 & 0.8123 & 0.8106 & 0.7765 & 0.8303 & **0.8403** \\ \hline \multirow{9}{*}{GCC} & Accuracy & 0.7723 & 0.7923 & 0.7942 & **0.7978** & 0.725 & 0.7327 & 0.6945 \\  & Fmeasure & 0.7867 & 0.8001 & 0.8021 & **0.8048** & 0.7476 & 0.7541 & 0.7199 \\  & Precision & 0.8197 & 0.8128 & 0.8153 & **0.8158** & 0.8202 & 0.8203 & 0.7989 \\  & Recall & 0.7723 & 0.7923 & 0.7942 & **0.7978** & 0.725 & 0.7327 & 0.6945 \\  & AUC & 0.785 & 0.7964 & 0.7969 & **0.7973** & 0.7805 & 0.7932 & 0.7722 \\ \hline \multirow{9}{*}{Eclipse} & Accuracy & 0.5514 & **0.5928** & 0.5887 & 0.5845 & 0.4966 & 0.5075 & 0.4863 \\  & Fmeasure & 0.5482 & **0.5961** & 0.5915 & 0.5877 & 0.4757 & 0.4853 & 0.46 \\ \cline{1-1}  & Precision & 0.7414 & **0.7307** & 0.7287 & 0.7217 & 0.7076 & 0.7282 & 0.7038 \\ \cline{1-1}  & Recall & 0.5514 & **0.5928** & 0.5887 & 0.5845 & 0.4966 & 0.5075 & 0.4863 \\ \cline{1-1}  & AUC & 0.7097 & **0.7342** & 0.7308 & 0.7287 & 0.6097 & 0.6477 & 0.6112 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: The results of GA(IS_FS)_imbalance and imbalance_GA(IS_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(IS\_Imbalance**} & \multicolumn{4}{c}{**Imbalance\_GA(IS)**} \\ \cline{3-9}  & & **RUS** & **ROS** & **CMA** & **SMOTE** & **RUS** & **ROS** & **SMOTE** \\ \hline \multirow{9}{*}{Mozilla} & Accuracy & 0.7738 & 0.7404 & 0.7505 & 0.7363 & 0.756 & 0.7579 & **0.7952** \\  & Fmeasure & 0.738 & 0.7401 & 0.7501 & 0.736 & 0.7559 & 0.7583 & **0.796** \\  & Precision & 0.7451 & 0.7462 & 0.7574 & 0.7427 & 0.7832 & 0.7795 & **0.8078** \\  & Recall & 0.7383 & 0.7404 & 0.7505 & 0.7363 & 0.756 & 0.7579 & **0.7952** \\  & AUC & 0.8042 & 0.811 & 0.8094 & 0.8107 & 0.8377 & 0.8402 & **0.8633** \\ \hline \multirow{9}{*}{GCC} & Accuracy & 0.7668 & 0.7887 & **0.7887** & 0.7832 & 0.6927 & 0.7382 & 0.7218 \\  & Fmeasure & 0.7808 & 0.7948 & **0.7977** & 0.7907 & 0.7185 & 0.7565 & 0.7433 \\ \cline{1-1}  & Precision & 0.8103 & 0.8036 & **0.8109** & 0.802 & 0.8028 & 0.8001 & 0.8019 \\ \cline{1-1}  & Recall & 0.7668 & 0.7887 & **0.7887** & 0.7832 & 0.6927 & 0.7382 & 0.7218 \\ \cline{1-1}  & AUC & 0.8019 & 0.81 & **0.8108** & 0.8071 & 0.7621 & 0.7774 & 0.7861 \\ \hline \multirow{9}{*}{Eclipse} & Accuracy & 0.5483 & **0.5779** & 0.5766 & 0.5717 & 0.5055 & 0.526 & 0.5196 \\ \cline{1-1}  & Fmeasure & 0.5414 & **0.5779** & 0.5766 & 0.5708 & 0.4836 & 0.5104 & 0.4959 \\ \cline{1-1}  & Precision & 0.7321 & **0.7368** & 0.735 & 0.734 & 0.7237 & 0.7304 & 0.7553 \\ \cline{1-1}  & Recall & 0.5483 & **0.5779** & 0.5766 & 0.5717 & 0.5055 & 0.526 & 0.5196 \\ \cline{1-1}  & AUC & 0.7256 & **0.7417** & 0.7404 & 0.7327 & 0.6645 & 0.7063 & 0.717 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: The results of GA(FS_IS)_imbalance and imbalance_GA(FS_IS) on the the Mozilla, GCC and Eclipse datasets.