# 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} \hline \hline \multirow{2}{*}{**Datasets**} & \multirow{2}{*}{**Classifiers**} & \multirow{2}{*}{**Origin**} & \multicolumn{6}{c}{**Feature Reduction**} & \multicolumn{6}{c}{**Instance Reduction**} \\  & & & **Metrics** & **Origin** & **GAFS** & **IG** & **CHI** & **OneR** & **Relief** & **GAIS** & **CNN** & **ENN** & **MCS** & **ICF** \\ \hline \multirow{10}{*}{Mozilla} & & Accuracy & 0.6882 & 0.6694 & 0.6957 & 0.6883 & 0.6469 & 0.6531 & 0.7085 & 0.6568 & 0.6863 & 0.7048 & 0.6863 \\  & & Frames & 0.6883 & 0.6684 & 0.6915 & 0.6836 & 0.6422 & 0.6398 & 0.7097 & 0.6568 & 0.6873 & 0.7056 & 0.6871 \\  & & Precision & 0.711 & 0.669 & 0.7491 & 0.7426 & 0.6497 & 0.7412 & 0.7165 & 0.6971 & 0.701 & 0.7215 & 0.7037 \\  & & Recall & 0.6882 & 0.6694 & 0.6957 & 0.6883 & 0.6469 & 0.6531 & 0.7085 & 0.6568 & 0.6863 & 0.7048 & 0.6863 \\  & & AUC & 0.7557 & 0.7082 & 0.8279 & 0.8298 & 0.656 & 0.7933 & 0.7754 & 0.7414 & 0.7489 & 0.7897 & 0.7514 \\ \cline{2-11}  & & Accuracy & 0.7565 & 0.7444 & 0.7607 & 0.7681 & 0.649 & 0.7087 & **0.8026** & 0.7454 & 0.7306 & 0.7583 & 0.7712 \\  & & Frames & 0.755 & 0.7439 & 0.7615 & 0.7689 & 0.6476 & 0.7069 & **0.803** & 0.7436 & 0.7276 & 0.7571 & 0.7030 \\  & & Precision & 0.7969 & 0.7523 & 0.7772 & 0.7847 & 0.6559 & 0.749 & **0.8238** & 0.7867 & 0.7794 & 0.7963 & 0.8076 \\  & & Recall & 0.7565 & 0.7444 & 0.7607 & 0.7681 & 0.649 & 0.7087 & **0.8026** & 0.7454 & 0.7306 & 0.7583 & 0.7712 \\  & & AUC & 0.8249 & 0.8082 & 0.8447 & 0.851 & 0.732 & 0.7985 & **0.8545** & 0.8006 & 0.8067 & 0.8409 & 0.8271 \\ \cline{2-11}  & & Accuracy & 0.6476 & 0.6471 & 0.7032 & 0.7013 & 0.649 & 0.6735 & 0.6494 & 0.6107 & 0.6162 & 0.6199 & 0.631 \\  & & Pressure & 0.6473 & 0.6472 & 0.7041 & 0.7026 & 0.6483 & 0.6746 & 0.6509 & 0.596 & 0.6178 & 0.6041 & 0.6321 \\ KNN & & Precision & 0.671 & 0.6476 & 0.7204 & 0.7129 & 0.6486 & 0.6894 & 0.6587 & 0.6809 & 0.6243 & 0.6979 & 0.6343 \\  & & Recall & 0.6476 & 0.6471 & 0.7032 & 0.7013 & 0.6496 & 0.6735 & 0.6494 & 0.6107 & 0.6162 & 0.6199 & 0.631 \\  & & AUC & 0.6828 & 0.6487 & 0.7177 & 0.7264 & 0.6594 & 0.7021 & 0.6861 & 0.6656 & 0.5918 & 0.6181 & 0.6203 \\ \hline \multirow{10}{*}{SVM} & & Accuracy & 0.7804 & 0.714 & 0.8145 & 0.8219 & 0.7041 & 0.7718 & 0.7583 & 0.7472 & 0.7565 & 0.7694 & 0.786 \\  & & Frames & 0.7814 & 0.7093 & 0.8153 & 0.8227 & 0.6998 & 0.7724 & 0.7592 & 0.7483 & 0.757 & 0.7697 & 0.7868 \\ \cline{1-1}  & & Precision & 0.7881 & 0.7206 & 0.827 & 0.8326 & 0.7102 & 0.7917 & 0.7634 & 0.7536 & 0.7584 & 0.7911 & 0.7919 \\ \cline{1-1}  & & Recall & 0.7804 & 0.714 & 0.8145 & 0.8219 & 0.7041 & 0.7718 & 0.7583 & 0.7472 & 0.7565 & 0.7694 & 0.786 \\ \cline{1-1}  & & AUC & 0.7847 & 0.708 & 0.8223 & 0.8288 & 0.6995 & 0.7826 & 0.7603 & 0.75 & 0.7552 & 0.7807 & 0.7891 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The results of feature selection and instance selection in the Mozilla dataset compared to the results of the raw Mozilla data.