# Performance Improvement of Decision Tree: A Robust Classifier Using Tabu Search Algorithm
## 2 Existing Classifiers and Their Limitations  
**Classifiers based on the DT algorithm. ** Initially, the DT algorithm was proposed to achieve higher accuracy with a reduced computational cost [11; 20; 21]. The accuracy varies with the robustness of the computational algorithm over the targeted data. The earlier DT algorithm, termed ID3, is more convenient for making simple decision trees. It resultsin a decrease in accuracy when the computational complexity increases. The most recent solutions for the accuracy improvement of the DT algorithm is described in [10; 20; 22; 23; 24; 25]. In [10], an intelligent classifier based on a decision tree algorithm was structured for diverse applications. Recently, the solution given in [20] proposed an optimized structure of the decision tree to improve the efficiency and to reduce the error rate by simplifying 19 nodes to five node structures. The results reveal that the accuracy of ID3 was 6% to 7% higher as compared to conventional supervised machine learning classifiers. The solution described in [22] proposed an optimized genetic algorithm by merging the DT algorithm with a parallel genetic decision tree. Towards performance improvement, the solution given in [23] has utilized a linear regression decision tree algorithm for data classification. To evaluate the effectiveness of their approach, a student evaluation and zoo datasets from the UCI Machine Learning Repository were considered. In [24], several classification models were trained to extract the relevant features from the targeted clinical or laboratory data. To achieve higher performance, relevant features from the different datasets were merged to construct a new dataset to train the learning model. A scalable decision tree algorithm was employed in [25] to classify the large dataset with improved efficiency as compared with other methods (such as SLIQ, SPRINT and RainForest). The efficiency was improved by reducing the sorting cost of the decision tree. **Classifiers based on combined DT and tabu search algorithms. ** Limited solutions are available in the literature where both DT and tabu search (TS) algorithms are utilized at the same time to improve the performance and accuracy of the learning classifiers [26; 27; 28; 29; 30]. In [26], a new algorithm was proposed to optimize the feature selection of hierarchical classification trees. To achieve this, a binary hierarchical classifier (BHC), also named the decision tree, was employed in this work. The result shows an improved accuracy when compared to traditional classifiers. Similarly, the solution described in [27], proposed a new classifier (they termed this \"ad hoc\") for logistic regression and discriminant analysis. This results in better accuracy as compared to conventional regression methods (i.e., stepwise, backward and forward). The authors in [28] proposed an algorithm for classifying large datasets. Their classifier outperformed as compared to nominal classifiers in terms of accuracy. Moreover, the achieved accuracy was validated by employing a well-known dataset containing information relevant to different chronic diseases. An interesting classifier was proposed in [29] where better accuracy was achieved when compared to multivariate decision trees. In fact, the execution time linearly increased with the increase in the size of a dataset. Finally, the authors in [30] described a new algorithm (that they termed \"cooperative-tabu-search\") for classifying important information into different categories. It iteratively reduces the leaf nodes and transforms the data into new space. The results after several experiments reveal that their classifier achieves better accuracy as compared to conventional classifiers. In a nutshell, the work given in [26; 27; 28; 30] reports improved accuracy while the solution proposed in [29] outperformed in terms of both accuracy and execution time. **Classifiers have also been employed for the detection and prediction of COVID-19.