# 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?
For the GCC dataset, the accuracy, F-measure, the precision, recall and AUC values are 0.7855, 0.7981, 0.827, 0.7855, and 0.8052, respectively. For the Eclipse dataset, the GA(IS)_imbalance method (that is, the instance selection of the dataset is first performed, and then the SMOTE method is used to imbalance the dataset) performs better for identifying high-impact bug reports. The accuracy, F-measure, precision, recall and AUC values are 0.6061, 0.6092, 0.7465, 0.6061 and 0.7475, respectively. From Table 12, we can find that the imbalance_GA(FS_IS) method (that is, first the SMOTE method is used to imbalance the dataset, then feature selection is used to reduce it, and then the instance selection is also used to reduce it) achieves the maximum performance improvement for identifying high-impact bug reports for the Mozilla dataset. The accuracy, F-measure, precision, recall and AUC values are 0.7952, 0.796, 0.8078, 0.7952, and 0.8633, respectively. For the GCC dataset, we find that the imbalance_GA(FS_IS) method (that is, first the CMA method is used for the imbalanced dataset, then feature selection is used for reduction, and then use instance selection is used for reduction) can achieve the maximum performance improvement for identifying high-impact bug reports and the accuracy, F-measure, precision, recall, and AUC values are 0.7887, 0.797, 0.8109, 0.7887, and 0.8108, respectively. For the Eclipse dataset, we find that the imbalance_GA(FS_IS) method (that is, first imbalance the dataset using the ROS method, then use the feature selection to reduce, and then use the instance selection to reduce) could achieve the maximum performance improvement for identifying high-impact bug reports, and the accuracy, F-measure, precision, recall, and AUC values are 0.5779, 0.7368, 0.5779, and 0.7417, respectively. From Table 13, we find that the GA(IS_FS)_imbalance method (that is, first instance selection is used, then feature selection is performed, and then the imbalanced learning strategy is used to imbalance the dataset) can achieve the best classification effect. For the Mozilla and Eclipse datasets, the accuracy, F-measure, precision, recall, and AUC values after the imbalance processing using the ROS method are 0.7724, 0.7734, 0.7889, 0.7724, and 0.8303 and 0.5928, 0.5961, 0.7307, 0.5928, and 0.7342, respectively. For the GCC datasets, the accuracy, F-measure, precision, recall and AUC values are 0.7788, 0.8054, 0.7778, and 0.799, respectively. For the Eclipse dataset, the GA(FS)_imbalance method (that is, feature selection is used first, and then the ROS method is used to imbalance the dataset) can achieve the best classification effect. For the Mozilla and Eclipse datasets,  
values after the imbalanced processing using the SMOTE method are 0.7978, 0.8048, 0.8158, 0.7978, and 0.7973, respectively. Therefore, we found that the imbalance_GA(FS_IS) method (that is, first use the ROS method to imbalance the dataset, then use the feature selection to reduce, and then use the instance selection to reduce) achieved the highest AUC value of 0.86333 for identifying high-impact bug reports for the Mozilla dataset, and using the CMA method for processing datasets after instance reduction achieved the highest precision value of 0.827 for identifying high-impact bug reports for the GCC dataset. For the Eclipse dataset, using the ROS method for processing datasets after FS and IS reduction simultaneously achieved the highest AUC value of 0.7761 to identify high-impact bug reports.