# Class-Level Refactoring Prediction by Ensemble Learning with Various Feature Selection Techniques
## 4 Results and Discussion
As base classifiers, we applied a decision tree, enhanced K-NN classifier, Logistic regression, SVM-Linear, SVM-Polynomial, SVM-RBF, LSSVM with different kernels, ANN-GD, ANN-GDX, ANN-LM, ANN-RBF, ELM with different kernels. Differing from existing approaches where authors split data into multiple chunks and apply different classifiers for ensemble design, we applied the same feature metrics as input-to-all classifiers and performed two-class classification. This method labeled each class as refactoring-prone and non-refactoring, labeled as 1 and 0, respectively. Thus, applying MVE for each class, we automatically classified that class or code component. The prime motive of applying multiple classifiers and their variants was to gain the maximum possible opinion towards refactoring a class's proneness to make an optimal classification. To enhance accuracy, we applied f5-fold cross-validation that resulted in better performance as 99.76. The proposed system was developed using MATLAB2019b, simulated over Microsoft OS with i3 and 4 GB RAM. It is commonly assumed that ensemble classifiers will outperform classical base learners or single machine learning methods in ensemble classification methods. This paper investigated relative performance by estimating each base learner's performance using the proposed heterogeneous ensemble learner. To achieve it for each classifier, false positives and false negatives (FN) were mapped out into a confusion matrix, which identifies the real positives and false negatives (FN). We used statistical measures such as classification or prediction accuracy, precision, recall, and F-Measure for calculating these matrix values for each base learner and ensemble classifier. The definitions of these performance variables are given in Table 2. During multiple number of times execution, precision measurement is vital for finding the best result in proposed model. Even the authors themselves may not be aware of the degree of precision their algorithms in their current implementation converges. Without knowing the convergence level of all the data, comparing multiple methodologies is not a valid method and could lead to incorrect conclusions. If at one 10-time execution, the obtained least and peak values for our proposed model with MVE are 0.0017 and 0.0123, respectively, then we can observe that the average precision of our model is 0.0140. We have applied the same approach utilized in the paper [58] to evaluate the precision of our model. Different base classifiers and ensemble models' statistical performance has been measured (i.e., decision tree, K-NN classifier, Logistic regression, SVM-Linear, SVM-Poly, SVM-RBF, LSSVM, LSSVM-Lin, LSSVM-Poly, LSSVM-RBF, ANN-GD, ANN-GDX, ANN-LM, ANN-RBF, ELM-Lin, ELM-Poly, ELM-RBF, MVE, and BTE). Observing the above-derived results, it can be easily found that the proposed ensemble learning method's accuracy is higher than any other base classifier. The performance of all the classifiers is shown in Table 3, and from the results, we can conclude that MVE achieves a better result than all kinds of individual frequently used classifiers. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Mathematical Expression** & **Definition** \\ \hline Accuracy & \(\frac{(TN+TP)}{(TN+TN+FP+TP)}\) & This calculation signifies the proportion of projected \\  & & refactoring-resistant modules that are inspected out of all modules. \\ \hline Precision & \(\frac{TP}{(TP+FP)}\) & Specifies the extent to which repeated tests produce the same \\  & & findings under unchanged conditions. \\ \hline F-measure & \(\frac{Recall\_Precision}{Recall+Precision}\) & It takes the recall and precision numbers and makes a single \\  & & number, the harmonic mean of those two. \\ \hline Recall & \(\frac{TP}{(TP+FN)}\) & It displays how many things there are to be picked. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance Parameters.