# 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?
From Table 6, it can be found that for the GCC dataset, the GA(FS) method based on the NBM classifier has the greatest improvement in the accuracy rate, recall rate and the AUC value. Among them, the accuracy of the GA(FS) method based on the NBM classifier for identifying high-impact bug reports is 0.7814, the recall rate is 0.7814, and the AUC value is 0.8. From Table 7, for the Eclipse dataset, using the OneR reduction method based on the NBR classifier to identify high-impact bug reports has the greatest improvement, especially for the accuracy, F-measure, recall and AUC values; the classification results are 0.6399, 0.6467, 0.6399, 0.7707, respective. Therefore, according to the experimental results, the reduced dataset has better classification performance than the unreduced dataset. RQ2: How Does the Order of the Feature Selection and Instance Selection on the Datasets Impact the Experimental Performance? In this research question, we want to investigate whether the order of the feature selection and instance selection on the Mozilla, GCC and Eclipse datasets has an impact on the experimental results. We consider four feature selection methods, i.e., IG, CHI, OneR, and Relief, and four instance selection methods, i.e., CNN, ENN, MCS, and ICF. We use the five evaluation metrics mentioned above (accuracy, precision, recall, F-measure and AUC) to analyze the experimental results. To answer this question, first, we use the genetic algorithm to simultaneously perform the approximate reduction of features and instances on the original dataset, i.e., GA(FS+IS), and record the experimental results. Thus, we use genetic algorithms to apply the feature selection for the datasets. Based on the obtained optimal individuals, we use the genetic algorithm to apply the instance selection, i.e., GA(FS_IS), and the experimental results are recorded. Next, we use genetic algorithms to apply the instance selection for the datasets. Based on the obtained optimal individuals, we use the genetic algorithm to apply the feature selection, i.e., GA(IS_FS), and record the experimental results. Finally, according to the first experiment, among the NB, NBM, KNN, and SVM classifiers, NBM has the best classification effect, so we only retain the experimental results of using NBM as a classifier in Table 8.  
Table 8 shows that the best classification performance is obtained after instance reduction using NBM as a classifier to identify high-impact bug reports for Mozilla dataset. The accuracy, F-measure, and recall values show the most obvious improvements, with values of 0.8026, 0.803, and 0.8026, respectively. For the GCC dataset, the GA(FS_IS) method (this is, feature selection is first performed on the dataset, and then the instance selection of the dataset is performed) achieves the best classification performance in identifying high-impact bug reports. In the accuracy value and the recall value, there is the most obvious improvement: the accuracy and the recall values are 0.796 and 0.796, respectively. For the Eclipse dataset, the GA(IS + FS) method (that is, feature selection and instance selection are performed simultaneously) performs better to identify high-impact bug reports. The most obvious improvement is obtained for the accuracy, F-measure and recall values, which are 0.6907, 0.6983 and 0.6907, respectively. Therefore, we found that the order of the feature selection and instance selection has an impact on identifying high-impact bug reports through the experiments on the Mozilla, GCC and Eclipse datasets. RQ3: What Is the Effect of the Balance Processing and Reduction Denoising Order on the Experimental Results? In this research question, we want to investigate the effect of the balance processing and reduction denoising order on the experimental results for the Mozilla, GCC and Eclipse datasets.