# 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 c c c c c c} \hline \hline \multirow{3}{*}{**Datasets**} & \multirow{3}{*}{**Classifiers**} & \multirow{3}{*}{**Origin**} & \multicolumn{6}{c}{**Feature Reduction**} & \multicolumn{6}{c}{**Instance Reduction**} \\ \cline{4-14}  & & & **GA(FS)** & **IG** & **CHI** & **OneR** & **Relief** & **GA(IS)** & **CNN** & **ENN** & **MCS** & **ICF** \\ \hline \multirow{10}{*}{CGC} & & Accuracy & 0.6273 & 0.5883 & 0.5748 & 0.5748 & 0.5066 & 0.5527 & 0.6509 & 0.6436 & 0.6255 & 0.6273 & 0.6545 \\  & & \multirow{3}{*}{NB} & \multirow{3}{*}{Freeasure} & 0.6592 & 0.6227 & 0.6096 & 0.6096 & 0.5402 & 0.5853 & 0.6798 & 0.6739 & 0.6569 & 0.6591 & 0.6818 \\  & & \multirow{3}{*}{NB} & \multirow{3}{*}{Precision} & 0.7705 & 0.7689 & 0.7709 & 0.7709 & 0.7703 & 0.7749 & 0.7559 & 0.7434 & 0.7348 & 0.7778 & 0.7449 \\  & & \multirow{3}{*}{Recall} & 0.6273 & 0.5883 & 0.5748 & 0.5748 & 0.5066 & 0.5527 & 0.6509 & 0.6436 & 0.6255 & 0.6273 & 0.6545 \\  & & & AUC & 0.7337 & 0.7482 & 0.7562 & 0.7651 & 0.7214 & 0.7431 & 0.6977 & 0.7321 & 0.6692 & 0.7393 & 0.6818 \\ \hline \multirow{10}{*}{CGC} & & Accuracy & 0.7582 & **0.7814** & 0.7755 & 0.7755 & 0.7865 & 0.7291 & 0.776 & 0.7737 & 0.7782 & 0.7255 & 0.7636 \\  & & \multirow{3}{*}{Fmeasure} & 0.7719 & **0.7867** & 0.7796 & 0.7746 & 0.7419 & 0.7751 & 0.7803 & 0.79 & 0.7465 & 0.7766 \\  & & \multirow{3}{*}{Precision} & 0.7994 & **0.7937** & 0.7846 & 0.7846 & 0.7874 & 0.7624 & 0.809 & 0.8069 & 0.8136 & 0.8032 & 0.8019 \\  & & \multirow{3}{*}{Recall} & 0.7582 & **0.7814** & 0.7755 & 0.7755 & 0.7685 & 0.7291 & 0.76 & 0.7673 & 0.7782 & 0.7255 & 0.7376 \\  & & \multirow{3}{*}{AUC} & 0.7769 & **0.8** & 0.7298 & 0.7703 & 0.7374 & 0.7872 & 0.7811 & 0.7834 & 0.7532 & 0.7702 \\ \cline{2-11}  & & \multirow{3}{*}{Accuracy} & 0.7109 & 0.7577 & 0.7536 & 0.7564 & 0.7647 & 0.8018 & 0.7218 & 0.7091 & 0.7545 & 0.6291 & 0.7382 \\ \cline{2-11}  & & \multirow{3}{*}{Fmeasure} & 0.7059 & 0.7581 & 0.7533 & 0.7533 & 0.7516 & 0.7977 & 0.7097 & 0.697 & 0.7213 & 0.6604 & 0.7294 \\ \cline{2-11}  & & \multirow{3}{*}{Precision} & 0.7013 & 0.7585 & 0.7529 & 0.7529 & 0.7431 & 0.7946 & 0.7004 & 0.6874 & 0.7083 & 0.7937 & 0.7225 \\ \cline{2-11}  & & \multirow{3}{*}{Recall} & 0.7109 & 0.7577 & 0.7536 & 0.7536 & 0.7647 & 0.8018 & 0.7218 & 0.7091 & 0.7545 & 0.6291 & 0.7382 \\ \cline{2-11}  & & \multirow{3}{*}{AUC} & 0.5625 & 0.6511 & 0.6417 & 0.6417 & 0.5947 & 0.6955 & 0.5576 & 0.5406 & 0.5519 & 0.6972 & 0.5905 \\ \hline \multirow{10}{*}{SVM} & & Accuracy & 0.8091 & 0.8033 & 0.8047 & 0.8029 & 0.797 & 0.8036 & 0.7818 & 0.7745 & 0.7982 & 0.7364 & 0.8087 \\ \cline{1-1}  & & \multirow{3}{*}{Fmeasure} & 0.803 & 0.7591 & 0.7611 & 0.7597 & 0.7477 & 0.749 & 0.7753 & 0.7833 & 0.7918 & 0.7544 & 0.8008 \\ \cline{1-1}  & & \multirow{3}{*}{Precision} & 0.7993 & 0.7899 & 0.7824 & 0.7782 & 0.7794 & 0.7957 & 0.7707 & 0.7975 & 0.7876 & 0.7956 & 0.7969 \\ \cline{1-1}  & & \multirow{3}{*}{Recall} & 0.8091 & 0.8033 & 0.8047 & 0.8029 & 0.797 & 0.8036 & 0.7818 & 0.7745 & 0.7982 & 0.7364 & 0.8073 \\ \cline{1-1}  & & \multirow{3}{*}{AUC} & 0.6937 & 0.586 & 0.5869 & 0.5857 & 0.561 & 0.5715 & 0.6554 & 0.719 & 0.6778 & 0.7212 & 0.6896 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The results of feature selection and instance selection in the GCC dataset compared to the results of the raw GCC data.