## 4 Experimental Results
### Experimental Series 1: Comparison with Single Classifiers  
In this subsection, the performance of OCATC on different datasets is compared to classifiers including LR, RF, KNN, DT, NN, SVC, LSVM, and SGD. The results are listed in Tables 4 and 5. For instance, Table 4 presents the optimal configurations for each dataset obtained by OCATC, where CA stands for the selected classifier, FS method represents the feature selection method, and FN represents the number of features. Table 5 reports the results of OCATC and comparison of different evaluated single classifiers. As shown in Table 4, the LSVM classifier shows the best performance across three datasets, namely, DatasetA, DatasetB, and DatasetF. On the other hand, PCA seems to work well with LSVM in selecting the optimal features and boosting the performance of LSVM on DatasetB and DatasetF. Considering the selected number of features (FN) per dataset, the IG algorithm only selected 36 relevant features for DatasetC from a total number of 1000 features. In contrast, RFF, PCA, SVD, and Chi2 selected a relatively larger number of features for the rest of the datasets, which seems to provide the highest performance alongside SVM-based classifiers. From the balanced datasets perspective, as shown in Table 5, DT did not perform well on balanced datasets such as DatasetA, DatasetD, and DatasetF, compared with other \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **CA** & **FS Method** & **FN** & **Micro-f1** & **Macro-f1** \\ \hline DatasetA & LSVM & RFF & 876 & 0.975 & 0.975 \\ DatasetB & LSVM & PCA & 436 & 0.936 & 0.929 \\ DatasetC & RF & IG & 36 & 0.972 & 0.925 \\ DatasetD & SVC & SVD & 289 & 0.967 & 0.967 \\ DatasetE & SVC & Chi2 & 942 & 0.975 & 0.975 \\ DatasetF & LSVM & PCA & 189 & 0.962 & 0.909 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of the optimal configurations obtained by OCATC. \begin{table}
\begin{tabular}{c c} \hline \hline
**Classifier** & **Parameters** \\ \hline LR & random\_state = False. \\ RF & n\_estimators = 50, random\_state = False. \\ KNN & n\_neighbors = 6. \\ DT & random\_state = False. \\ NN & solver = ’bfgs’, alpha = 1e\(-\)5, hidden\_layer\_sizes = (15), random\_state = False. \\ SVC & random\_state = False, kernel = ’linear’, C = 1, gamma = 1, degree = 5. \\ LSVC & random\_state = False, max\_iter = 5. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification algorithms parameters. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Dataset** & **Measure** & **OCATC** & **LR** & **RF** & **KNN** & **DT** & **NN** & **SVC** & **LSVM** & **SGD** \\ \hline \multirow{2}{*}{DatasetA} & micro-f1 & **0.975** & 0.971 & 0.96 & 0.948 & 0.847 & 0.971 & 0.973 & 0.973 & 0.968 \\  & macro-f1 & **0.975** & 0.971 & 0.96 & 0.948 & 0.847 & 0.971 & 0.973 & 0.973 & 0.968 \\  & micro-f1 & **0.936** & 0.927 & 0.893 & 0.902 & 0.769 & 0.912 & 0.934 & 0.933 & 0.932 \\  & macro-f1 & **0.929** & 0.918 & 0.872 & 0.89 & 0.738 & 0.903 & 0.927 & 0.925 & 0.922 \\  & micro-f1 & **0.972** & 0.865 & 0.95 & 0.841 & 0.94 & 0.878 & 0.887 & 0.896 & 0.876 \\  & macro-f1 & **0.925** & 0.714 & 0.869 & 0.763 & 0.891 & 0.834 & 0.848 & 0.821 \\  & micro-f1 & **0.967** & 0.963 & 0.945 & 0.921 & 0.827 & 0.956 & 0.957 & 0.959 & 0.935 \\  & macro-f1 & **0.967** & 0.963 & 0.945 & 0.921 & 0.826 & 0.956 & 0.958 & 0.959 & 0.935 \\  & micro-f1 & **0.974** & 0.973 & 0.961 & 0.948 & 0.901 & 0.963 & 0.974 & 0.973 & 0.973 \\  & macro-f1 & **0.974** & 0.973 & 0.961 & 0.948 & 0.901 & 0.963 & 0.974 & 0.973 & 0.973 \\  & macro-f1 & **0.962** & 0.928 & 0.934 & 0.931 & 0.835 & 0.935 & 0.955 & 0.956 & 0.948 \\  & macro-f1 & **0.909** & 0.791 & 0.817 & 0.87 & 0.764 & 0.872 & 0.887 & 0.897 & 0.889 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of OCATC (optimal configurations) and single classifiers. classifiers and OCATC. In single classifier training, it should be noted that the total number of 1000 features were used. LR and DT show the worst performance in terms of macro-f1 on DatasetC and DatasetF, respectively.