# Performance Improvement of Decision Tree: A Robust Classifier Using Tabu Search Algorithm
## 6 Experimental Results and Performance Evaluations
### Performance Comparison
#### 6.4.2 Comparison to State-of-the-Art Classifier Algorithms  
The performance comparison with the most recent state-of-the-art classifiers in terms of execution time and accuracy is presented in Table 3. In order to train the model, the classifiers, reported in [34,36,38,50], employed different classification approaches while the classifiers, reported in [23,28,30], consider decision tree optimizations using the tabu search algorithm. Note that we used the \"-\" in Table 3 where the relevant information is not provided. **Comparison to classifiers given in [34,36,38,50]. ** In [50], the employed classifiers are NB, SVM, RF and KNN. For these classifiers (NB, SVM, RF and KNN), the reported accuracy in Table 3 are 0.98%, 69.79%, 77.98% and 75.68%. Our proposed TSODT classifier results to be 100 (for NB), 1.40 (for SVM), 1.25 (for RF) and 1.29 (for KNN) times higher in accuracy when compared to [50]. Apart from accuracy, the execution time in [50] is only reported for the NB classifier (0.96 ms). This is comparatively 1.02 times higher than in this work (0.94). Recently, the most interesting work is described in [36], where utilized classifiers are SVM, RF, and DT. For these classifiers (SVM, RF and DT), the reported values for accuracy in Table 3 are 89%, 94.3% and 0.91%. The proposed TSODT classifier result 1.10 (ratio of 98 over 89 for SVM), 1.03 (ratio of 98 over 94.3 for RF) and 107.69 (ratio of 98 over 0.91 for DT) times higher accuracy as compared to [36]. Similar to [50], the execution time in [36] is only given for the RF classifier (0.93 ms). This is comparatively 1.02 times lower than in this work (0.95). In [38], the employed classifiers are NB, LR, SVM and KNN. For the corresponding classifiers, the achieved accuracies are 94.3%, 94.41%, 92.4% and 89.2% (shown in Table 3). As compared to [38], our proposed TSODT classifier result 1.03 (for NB), 1.03 (for LR), 1.06 (for SVM) and 1.09 (for KNN) times higher accuracy. Similarly to [38], the work reported in [34] utilizes different classifiers (i.e., NB, LR, SVM, RF, and DT) for the detection of COVID-19. For the performance comparisons, only the values for accuracy are provided, as shown in Table 3. Comparatively, the proposed TSODT classifier algorithm is 1.01, 1.01, 1.08, 1.03 and 106.52 times faster (in terms of accuracy) than NB, LR, SVM, RF and DT algorithms, respectively. **Comparison over the optimized decision tree using the tabu search algorithm [23,28,30]. ** As shown in Table 3, the authors in [23] achieved 92.5% accuracy for their proposed optimized \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Classifiers**} & \multicolumn{2}{c}{**Our Work**} & \multicolumn{2}{c}{**2020, [50]**} & \multicolumn{2}{c}{**2021, [36]**} & \multicolumn{2}{c}{**2020, [38]**} & \multicolumn{2}{c}{**2020, [34]**} \\ \cline{2-11}  & **Acc (\%)** & **ET (ms)** & **Acc (\%)** & **ET (ms)** & **Acc (\%)** & **ET (ms)** & **Acc (\%)** & **ET (ms)** & **Acc (\%)** & **ET (ms)** \\ \hline NB & 96.6 & 0.94 & 0.98 & 0.96 & – & – & 94.36 & 0 & 96.2 & – \\ \hline LR & 97.1 & 0.82 & – & – & – & – & 94.41 & 0 & 96.2 & – \\ \hline TSODT & 98 & 0.97 & – & – & – & – & – & – & – & – \\ \hline SVM & 89.2 & 0.81 & 69.79 & – & 89 & – & 92.4 & – & 90.6 & – \\ \hline RF & 95 & 0.95 & 77.98 & – & 94.3 & 0.93 & – & – & 94.3 & – \\ \hline KNN & 87 & 0.90 & 75.68 & – & - & - & 89.2 & – & – \\ \hline DT & 93.6 & 55.6 & – & – & 0.91 & – & – & 0.92 & – \\ \hline DT (Optimized) [23] & 92.5 & L & – & – & – & – & – & – & – \\ \hline DT (Optimized) [28] & 96.0 & – & – & – & – & – & – & – \\ \hline DT (Optimized) [30] & 95.5 & L & – & – & – & – & – & – & – \\ \hline \hline \end{tabular}  
**Acc: accuracy; ET execution time; L this shows a linear relationship of execution time with datapoints.**  
\end{table} 
Table 3: Comparison to state-of-the-art classifiers. algorithm. This is comparatively 1.05 times lower than in this work. Moreover, there is a linear relationship between the execution time with datapoints.