# Performance Improvement of Decision Tree: A Robust Classifier Using Tabu Search Algorithm
## 6 Experimental Results and Performance Evaluations
### Performance Comparison
#### 6.4.1 Comparison to Conventional Classifiers  
As shown in column two of Table 2, the most efficient classifier with respect to execution time (or performance) is NB as it trains the model in 8.5 ms. This is due to a requirement for the computation of prior probabilities. Once all the probabilities are calculated, they can be stored in a storage memory for efficient use in the next required computations. On the other hand, SVM is shown to slow down as compared to other classifiers in Table 2. This is because it requires to load the whole dataset in a RAM and thereafter, process each data point sequentially. It is important to worth mentioning that the selected clinical dataset for training in this work is quite large. Therefore, the proposed TSODT classifier algorithm (Algorithm 1) requires 1.79, 224.7 and 9.09 times lower execution/computational time to train the model as compared to LR, SVM and RF classifiers, respectively. Both the proposed TSODT and DT classifier algorithms take the same execution time (55.6 ms). On the other hand, the proposed classifier algorithm (Algorithm 1) requires a 6.54 and 3.97 times higher execution time as compared to the conventional NB and KNN classifier algorithms, respectively.  
The proposed TSODT classifier algorithm efficiently performs over a large number of features. Therefore, for the same number of features (fifteen), our proposed TSODT classifier algorithm (Algorithm 1) results in higher accuracy (98%, as can be seen in column seven of Table 2) as compared to other classifiers. It is important to worth mentioning that the higher the accuracy, the higher the classifier performance will be. As compared to other classifiers, i.e., NB, LR, DT, SVM, RF and KNN, the proposed TSODT classifier results as 1.01, 1.02, 1.04, 1.09, 1.02 and 1.04 times higher in accuracy. Moreover, the proposed TSODT algorithm aggregates a large number of decision trees, which limit the overfitting and error rate parameters due to certain bias.  
As shown in column eight of Table 1, the proposed TSODT algorithm results in an error rate of 3.7%. Therefore, when concerning only the error rate for the comparisons, our proposed classifier provides a 1.45, 2.97, 2.60, 2.97, 1.18 and 2.78 times lower error rate as compared to the NB, LR, DT, SVM, RF and KNN conventional classifier approaches,  
\begin{table}
\begin{tabular}{l r r r r r r r r r r} \hline \hline \multirow{2}{*}{**Classifier**} & \multicolumn{2}{c}{**Execution**} & \multirow{2}{*}{**TP**} & \multirow{2}{*}{**TN**} & \multirow{2}{*}{**FP**} & \multirow{2}{*}{**FN**} & \multirow{2}{*}{**Accuracy (\%)**} & \multirow{2}{*}{**Error Rate (\%)**} & \multirow{2}{*}{**Precision (\%)**} & \multirow{2}{*}{**Recall (\%)**} & \multirow{2}{*}{**Fscore (\%)**} & \multirow{2}{*}{**AUROC**} \\ \cline{1-1} \cline{6-12}  & & & & & & & & & & & & \\ \hline NB & 8.5 & 1400 & 1050 & 80 & 16 & 96.6 & 5.4 & 94.5 & 98.8 & 96.7 & 0.846 \\ \hline LR & 99.8 & 1550 & 905 & 60 & 10 & 97.1 & 11 & 82.4 & 98.2 & 89.6 & 0.89 \\ \hline DT & 55.6 & 1390 & 920 & 98 & 60 & 93.6 & 9.627 & 93.4 & 95.8 & 94.6 & 0.88 \\ \hline SVM & 12,496.9 & 1061 & 814 & 234 & 20 & 89.2 & 11 & 81.9 & 98.1 & 89.2 & 0.91 \\ \hline RF & 505.5 & 1067 & 1002 & 46 & 29 & 96.5 & 4.38 & 95.8 & 97.3 & 96.6 & 0.898 \\ \hline KNN & 14 & 1068 & 940 & 108 & 30 & 93.9 & 10.3 & 90.8 & 97.2 & 93.9 & 0.754 \\ \hline
**TSODT** & 55.6 & 1565 & 1020 & 40 & 10 & 98.1 & 3.7 & 97.5 & 99.3 & 98.4 & 0.95 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results and performance comparison to conventional state-of-the-art classifiers.  
respectively. As shown in columns nine to eleven in Table 3, our proposed TSODT classifier algorithm results in higher values for other parameters, i.e., precision (97.5%), recall (99.3%) and Fscore (98.4%).