# Class-Level Refactoring Prediction by Ensemble Learning with Various Feature Selection Techniques
## 4 Results and Discussion
#### Error Profiling  
As mentioned in the previous sections, ensemble learning aims to reduce prediction errors by amalgamating different classifiers and their respective performances over the original feature data. As a result of this, the proposed ensemble classifier has been evaluated in this paper in terms of different error profile parameters, such as mean absolute error (MAE), root means square error (RMSE) means relative error magnitude (MMSE), etc. The performance comparison was performed with other base classifiers to test the effectiveness of the proposed ensemble learning model refactoring prediction through various errors shown in Table 4. A snippet of the various error parameters and their respective mathematical models is discussed below.  
1. _Mean Absolute Error (MAE);_
2. _Standard Error of the Mean (SEM);_
3. _Mean Magnitude of the Relative Error (MORE);_
4. _Root Mean Square Error (RMSE)._  
Observing the results in Table 3, it can be found that the proposed ensemble classifier exhibits a minimum of the other base learners. It confirms the efficiency and robustness of the proposed ensemble classifier. Thus, considering the overall research outcomes, the projected ensemble classifier containing base learners can perform highly precise and consistent classification in Table 3. The proposed ensemble classifier has exhibited better performance for refactoring prediction that can eventually help developers and companies achieve reliable software development.  
We have shown the comparative study among feature selection and data sampling techniques in a boxplot diagram. Each ensemble classifier's performance in terms of accuracy has been also represented in the form of a boxplot in Figure 4. However, the numerous boxplots for examining outliers, skewness, interquartile range in accuracy, F-Measure, and AUC performance metrics for feature selection and data sample strategies are shown in Figures 5 and 6. The median value, which divides the box into two parts, is indicated by the red line in the boxplot of Figures 5 and 6. According to Figure 5, the median  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Techniques** & **Accuracy** & **Precision** & **Recall** & **F-Measure (\%)** \\ \hline Logistic regression & 70.73 & 69.93 & 71.93 & 70.91 \\ \hline Decision Tree & 69.81 & 71.32 & 73.51 & 72.35 \\ \hline ANN-GDX & 91.48 & 88.43 & 81.02 & 84.56 \\ \hline ANN-LM & 91.90 & 83.94 & 84.17 & 84.05 \\ \hline ANN-GD & 89.38 & 84.61 & 83.91 & 84.25 \\ \hline SVM-Lin & 69.84 & 70.90 & 70.06 & 70.47 \\ \hline SVM-Poly & 69.84 & 70.94 & 71.00 & 70.96 \\ \hline SVM-RBF & 69.84 & 69.91 & 69.99 & 69.94 \\ \hline LSSVM-Lin & 88.42 & 89.91 & 86.61 & 88.22 \\ \hline LSSVM-Poly & 88.05 & 88.94 & 89.59 & 89.07 \\ \hline LSSVM-RBF & 89.90 & 94.31 & 88.92 & 91.53 \\ \hline ELM-Lin & 89.73 & 90.84 & 90.62 & 90.72 \\ \hline ELM-Poly & 90.47 & 91.03 & 90.01 & 90.51 \\ \hline ELM-RBF & 93.50 & 91.07 & 91.10 & 91.08 \\ \hline MVE & **99.76** & **99.93** & **98.96** & **99.44** \\ \hline BTE & 99.56 & 99.16 & 98.10 & 98.62 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance values of ensemble classifiers. Bold identifies the best classifier’s result in the model.