# Performance Improvement of Decision Tree: A Robust Classifier Using Tabu Search Algorithm
## 6 Experimental Results and Performance Evaluations
### Performance Comparison
#### 6.4.3 Performance Validation of the Proposed TSODT Classifier Algorithm  
After providing comparisons in Sections 6.4.1 and 6.4.2, we validated the performance of our proposed TSODT algorithm for different data points, as shown in Figure 7. The x axis in the figure shows the number of datasets with different data points (in \(value\times 1000\)) while the y axis, shows the execution time (in ms). Consequently, the trend (presented in Figure 7) explicitly shows that there is a linear increase in the execution time as with the increase in the number of data points.  
**Evaluation of Big O**. We evaluated our classifier with other classifiers for linear and logarithmic Big O. Figure 8 illustrates the evaluation of logarithmic and linear Big O for TSODT and other traditional classifiers. The x axis of this figure shows the number of elements in a dataset and y axis shows the complexity of Big O in terms of execution time. It is clear that the algorithm complexity of TSODT is linear O(n) and this trend is depicted in algorithmic and logarithmic complexity.  
**Statistical analysis.** Statistical analysis was performed to compare the proposed classifier and traditional classifiers. For this, the Friedman test and a post hoc analysis were performed [51]. The results for classification algorithms using the dataset are presented in Table 4. This table compares the TSODT and other classifiers with each other in terms of average ranks. The listed numbers illustrate subsequent classification algorithms sorted by the accuracy in ascending order. TSODT is highlighted as a number 1, the sum of average ranks is maximum for TSODT. Consequently, TSODT outperforms as compared to other classifiers. On the other hand, the value of AUROC, as shown in the last column of Table 2, confirms that TSODT has the highest probability as compared to the other classifiers. The  
Figure 8: Performance validation (based on execution time) of our proposed TSODT classifier.  
Figure 7: Performance comparison different classifiers for logarithmic and linear Big O.  
results shows that NB and LR also offer better performance which shows high sensitivity and specificity.  
**Analysis of AUROC.** We also evaluated the performance of our classifier by comparing the AUROC of traditional and TSODT. Figure 9 illustrates the ROC curves for different classifiers. The ROC curve is generated by considering sensitivity and specificity. The x axis of this figure shows the false positive rate (FPR) and the y axis shows the true positive rate (TPR) of the corresponding classifier. The numeric values of AUROC are listed in the last column of Table 2. The AUROC for TSODT is higher as compared to other classifiers. On the other hand, the AUROC of KNN is the lowest among all classifiers. The evaluation of results is based on the COVID-19 dataset, which proves that our classifiers outperform. The values for AUROC might be different for other datasets but the comparative value will be higher. Generally speaking, the evaluation also shows that this classifier is well suited for medical applications.