## 5 Experimental Results and Discussion
### Conventional Machine Learning Experimental Results
#### 5.2.3 Active MQ Defect Data Set Experimental Results  
The Active MQ Defect Data experimental results of the classification algorithms are presented in Table 9. According to the experiments, no significant increase or decrease was seen in the SVM algorithm.  
On the other hand, ACC values of the algorithm Naive Bayes had reduced. However, ACC values of the remaining algorithms had increased. As seen in Table 6, the average eACC value of the five base classifiers is 91.93% and the average ACC is 91.80%. The Diff value that shows the difference between two primary metrics indicates a negative difference of 0.13%. This result can be attributed to the results of Naive Bayes' classification. Similarly, the average FM decreased. Nevertheless, AUC demonstrated a very small increase, from 0.910 to 0.913.  
The choice of the algorithm may vary based on several factors, such as the data type, problem complexity, and the desired level of accuracy. For instance, if the problem is a straightforward classification task with a modest amount of clean and structured data, algorithms such as 'KNN' or logistic regression may be satisfactory. Therefore, it might be  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Algorithm** & **ACC (\%)** & **AUC** & **FM** & **KE** \\ \hline [PERSON] & 88.77 & 0.779 & 0.902 & 0.253 \\ SVM & **94.00** & 0.507 & 0.912 & 0.025 \\ KSTAR & 92.57 & 0.786 & 0.921 & 0.244 \\ KNN & 91.57 & 0.671 & 0.912 & 0.185 \\ Random Tree & 92.77 & 0.668 & 0.918 & 0.190 \\ Average & 91.93 & 0.682 & 0.913 & 0.179 \\ \hline
**Algorithm** & **eACC (\%)** & **eAUC** & **eFM** & **eKE** \\ \hline Naive Bayes & 84.56 & 0.787 & 0.877 & 0.227 \\ SVM & 94.00 & 0.513 & 0.912 & 0.007 \\ KSTAR & 93.24 & 0.779 & 0.919 & 0.160 \\ KNN & 92.83 & 0.748 & 0.920 & 0.211 \\ Random Tree & **94.38** & 0.763 & 0.926 & 0.197 \\ Average & 91.80 & 0.718 & 0.910 & 0.160 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Classification results for Active MQ data set.  
reasonable to test various machine learning algorithms and compare their performance on the same problem to solve the problem and resolve which approach will perform better.  
Table 9 clearly shows that the Random Tree ensembles classifier has a higher ACC compared to the other classifiers. It has a better ACC in 94.38% among the five algorithms. The support vector machines (SVM) algorithm is a machine learning approach that generates quite successful outcomes, especially in multidimensional data sets. This algorithm aims to determine the most suitable plane (support vector) to classify the data points into two groups. The SVM algorithm potentially achieves more successful outcomes if the relationships between the properties are powerful. However, the Random Tree algorithm is a decision tree algorithm based on property values. This algorithm is also known for achieving a high performance with large data sets. The ensemble RF combines the predictions of multiple models to create a final prediction. It can create final predictions robustly and accurately by using multi-models. Thus, the errors made by individual models may be compensated for by other models in the ensemble, leading to a more accurate overall prediction. Therefore, using the ensemble Rotation Forest with precise and robust algorithms like SVM and Random Tree can further improve the accuracy and robustness of the final prediction.