# Class-Level Refactoring Prediction by Ensemble Learning with Various Feature Selection Techniques
## 5 Comparative Analysis  
This section summarizes the comparative analysis between different frequently used classifiers, ensemble classifiers, and deep learning classifiers. Many authors worked on refactoring prediction by individual machine learning classifiers and ensemble learning at the class and method levels shown in Tables 5 and 6. Table 5 also represents different authors' contributions towards the refactoring prediction development and their limitations too. Figure 5: Accuracy, F-Measure, and AUC Performance over different feature selection techniques. Figure 6: Accuracy, F-Measure, and AUC performance over different data samples. Figure 7: Accuracy Measurement for ensemble algorithms. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Author** & **No. of Metrics** & **Method Level/Class Level** & **Classifiers** & **Results** & **Limitations** \\ \hline [PERSON] et al. [59] & 102 code metrics & Class Level & Gated recurrent unit algorithm & Projects with balanced data achieve better results in comparison with projects with unbalanced data. & Authors should apply the deep learning algorithm with method-level refactoring to obtain a better result. & Authors can also apply deep learning techniques at the method and class level then there is a chance of performance \\ \hline [PERSON] et al. [60] & 102 code metrics & Class Level & LSSVM & The mean value of the Area Under Curve (AUC) for the LSSVM RBF kernel is 0.96. & Authors can also apply deep learning techniques at the method and class level then there is a chance of performance \\ \hline [PERSON] et. al. [61] & 30 code metrics & Class Level & Naive Bayes classifiers & Mean accuracy for the Gaussian, Bernoulli and Multinomial classifiers 33.33\%, 39\%, 48.33\%. & The authors should provide a comparative study and explain how naive Bayes differs from others with the same kernels. \\ \hline [PERSON] et. al. [62] & 103 code metrics & Method level & Naive Bayes classifiers & Mean accuracy (84\%) and AUC (0.78) state that BNB is superior then the other naive Bayes classifiers. & There should be comparative study among naive Bayes classifiers. \\ \hline [PERSON] et. al. [63] & 65 code metrics & Method level & SVM and Weighted SVM & SMOTE with SVM achieves good result as comparison to weighted SVM. & The authors have to consider some more parameters for measuring the model’s efficiency and should be considered in class level. \\ \hline [PERSON] et. al. [64] & 102 metrics & Class level & SVM with two optimized (GA and While) algorithm & SVM with GA and While achieves optimized (GA and While) algorithm & The authors need to calculate the AUC value because the data only SVM set needs to be more balanced. \\ \hline [PERSON] et. al. [64] & 102 metrics & Class level & Logistic Regression, Regression, Decision Tree, SVM, ELM, ANN with different kernels, LSSVM with different kernels, MVE & The authors need to calculate the AUC value because the data set needs to be more balanced. \\ \hline Our proposed model & 125 Source code metrics & Class level & Logistic Regression, Regression, Decision Tree, SVM, ELM, ANN with different kernels, LSSVM with different kernels, LSSVM with different kernels, MVE & MVE achieves a better result than others based on the method for the method level and field level. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Limitations of recently published papers by using frequently used classifiers. Researchers have presented many prediction methods based on source code metrics to forecast the change-proneness of classes. However, several of these models have low prediction accuracy due to large complexity or imbalanced classes in the data set. Recent research indicates that utilizing ensembles to combine multiple models, pick features, or execute sampling can overcome dataset difficulties and enhance prediction accuracy. This section is designed to offer a comparative study between the existing refactoring model and our proposed refactoring model.