# 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**} & \multicolumn{3}{c}{**Evaluation**} & \multicolumn{3}{c}{**Feature Reduction**} & \multicolumn{6}{c}{**Instance Reduction**} \\ \cline{3-14}  & & **Metrics** & **Origin** & **GA(FS)** & **IG** & **CHI** & **OneR** & **Relief** & **GA(IS)** & **CNN** & **ENN** & **MCS** & **ICF** \\ \hline \hline \multirow{11}{*}{Eclipse} & & Accuracy & 0.5911 & 0.5503 & 0.5679 & 0.5685 & 0.4684 & 0.4905 & 0.6055 & 0.587 & 0.5952 & 0.6089 & 0.5891 \\  & & Emesure & 0.6006 & 0.5486 & 0.5689 & 0.5689 & 0.4257 & 0.4714 & 0.6156 & 0.5954 & 0.6051 & 0.619 & 0.5989 \\  & & Precision & 0.6865 & 0.7102 & 0.714 & 0.7161 & 0.7037 & 0.695 & 0.6928 & 0.691 & 0.6866 & 0.6954 & 0.6817 \\  & & Recall & 0.5911 & 0.5503 & 0.5679 & 0.5685 & 0.4684 & 0.4905 & 0.6055 & 0.587 & 0.5952 & 0.6089 & 0.5891 \\  & & AUC & 0.6931 & 0.7129 & 0.7173 & 0.7177 & 0.7251 & 0.6829 & 0.6972 & 0.6857 & 0.6908 & 0.7006 & 0.6772 \\ \cline{2-14}  & & Accuracy & 0.5549 & 0.5745 & 0.5768 & 0.5788 & **0.6399** & 0.5693 & 0.5823 & 0.5597 & 0.5481 & 0.5427 & 0.5611 \\  & & Emesure & 0.5486 & 0.5776 & 0.5783 & 0.5804 & **0.6467** & 0.5718 & 0.5822 & 0.5528 & 0.5387 & 0.5323 & 0.5571 \\  & & Precision & 0.7331 & 0.7184 & 0.7225 & 0.7221 & **0.7348** & 0.7157 & 0.737 & 0.743 & 0.738 & 0.7342 & 0.7298 \\  & & Recall & 0.5549 & 0.5745 & 0.5768 & 0.5788 & **0.6399** & 0.5693 & 0.5823 & 0.5597 & 0.5481 & 0.5427 & 0.5611 \\  & & AUC & 0.723 & 0.7295 & 0.7037 & 0.7313 & **0.7707** & 0.705 & 0.7531 & 0.7218 & 0.5125 & 0.7202 & 0.7109 \\ \cline{2-14}  & & Accuracy & 0.6355 & 0.6848 & 0.7318 & 0.7212 & 0.7903 & 0.6847 & 0.6608 & 0.5782 & 0.6205 & 0.6198 & 0.5857 \\  & & Pressure & 0.6437 & 0.6895 & 0.732 & 0.7215 & 0.7009 & 0.6851 & 0.6614 & 0.59 & 0.6315 & 0.6312 & 0.5981 \\  & & Precision & 0.6599 & 0.6996 & 0.7323 & 0.7217 & 0.6996 & 0.6854 & 0.662 & 0.6177 & 0.6636 & 0.6711 & 0.6384 \\  & & Recall & 0.6355 & 0.6848 & 0.7318 & 0.7212 & 0.7093 & 0.6847 & 0.6608 & 0.5782 & 0.6205 & 0.6198 & 0.5857 \\  & & AUC & 0.6183 & 0.6617 & 0.7077 & 0.6906 & 0.6628 & 0.6531 & 0.6274 & 0.5688 & 0.6199 & 0.6293 & 0.5923 \\ \hline \multirow{11}{*}{SVM} & & Accuracy & 0.759 & 0.7545 & 0.7558 & 0.7568 & 0.721 & 0.7164 & 0.7352 & 0.729 & 0.7502 & 0.714 & 0.7447 \\  & & Emesure & 0.7498 & 0.7199 & 0.7211 & 0.727 & 0.6859 & 0.6546 & 0.7275 & 0.7327 & 0.74 & 0.7226 & 0.7372 \\ \cline{1-1}  & & Precision & 0.7504 & 0.7624 & 0.7669 & 0.7614 & 0.7146 & 0.723 & 0.7256 & 0.739 & 0.7405 & 0.7637 & 0.7357 \\ \cline{1-1}  & & Recall & 0.759 & 0.7545 & 0.7558 & 0.7568 & 0.721 & 0.7164 & 0.7352 & 0.729 & 0.7502 & 0.714 & 0.7447 \\ \cline{1-1}  & & AUC & 0.6978 & 0.6466 & 0.6492 & 0.6574 & 0.6219 & 0.5804 & 0.6768 & 0.7092 & 0.6864 & 0.7351 & 0.6871 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The results of feature selection and instance selection in the Eclipse dataset compared to the results of the raw Eclipse data. From Tables 5-7, it can be found that the ability to identifying high-impact bug reports by using feature selection and instance selection to select datasets after reduction works much better than the original experimental datasets. For example, from Table 5, it can be found that for the Mozilla dataset, the NBM classifier-based instance reduction method GA(IS) works best for identifying high-impact bug reports. It achieves the largest AUC value, which is 0.8545, and the accuracy, F-measure, precision, and recall values are 0.8026, 0.803, 0.8238, and 0.8026, respectively. Compared with the original data classification without data reduction, the F-measure value results have the greatest improvement. For the Mozilla dataset after attribute reduction with CHI, the SVM classifier has the best classification performance for identifying high-impact bug reports, and the accuracy, F-measure, precision, recall, and AUC values are 0.8219, 0.8227, 0.8326, 0.8219, 0.8288, respectively, and achieve the maximum accuracy, F-measure, precision and recall values.