# Performance Improvement of Decision Tree: A Robust Classifier Using Tabu Search Algorithm
###### Abstract  
Classification and regression are the major applications of machine learning algorithms which are widely used to solve problems in numerous domains of engineering and computer science. Different classifiers based on the optimization of the decision tree have been proposed, however, it is still evolving over time. This paper presents a novel and robust classifier based on a decision tree and tabu search algorithms, respectively. In the aim of improving performance, our proposed algorithm constructs multiple decision trees while employing a tabu search algorithm to consistently monitor the leaf and decision nodes in the corresponding decision trees. Additionally, the used tabu search algorithm is responsible to balance the entropy of the corresponding decision trees. For training the model, we used the clinical data of COVID-19 patients to predict whether a patient is suffering. The experimental results were obtained using our proposed classifier based on the built-in sci-kit learn library in Python. The extensive analysis for the performance comparison was presented using Big O and statistical analysis for conventional supervised machine learning algorithms. Moreover, the performance comparison to optimized state-of-the-art classifiers is also presented. The achieved accuracy of 98%, the required execution time of 55.6 ms and the area under receiver operating characteristic (AUROC) for proposed method of 0.95 reveals that the proposed classifier algorithm is convenient for large datasets.  
suprevised machine learning; decision tree; tabu search; performance improvement; execution time; accuracy +
Footnote †: journal: Applied Sciences  
are first trained using historical data. Then, the trained prediction model is employed in the designated application environment.  
The decision tree algorithm is one of the prominent techniques for machine learning, data mining and pattern recognition. These algorithms construct classification or regression models exploiting the partitioning of data based on regular recursion [10]. Generally, a decision tree algorithm starts with the complete dataset, breaks the dataset into two or more subsets on the basis of one or more than one attribute and then this process repeats until an appropriate number of finer subsets are generated. The modeled process could be represented as a tree structure and generally, it is summarized as a set of \"if-else\" statements [11]. The decision tree algorithm is easy to elucidate and it can handle noisy data. In the discipline of computer science and engineering, a decision tree is used for the diagnosis of medical diseases, electrical troubleshooting, image recognition, signal processing, etc. [10; 12]. Concerning its industrial applications, the decision tree algorithm is widely used for fraud detection, the management of customer data, payment evaluations, etc. Moreover, a decision tree is also employed in different data mining applications and it can aid in large dataset evaluation.  
We note that the aforementioned classification algorithms have the inability to adapt their dynamic behaviors or vary the conditions of different learning models [10; 13; 14; 15; 16; 17]. Therefore, there is a real need for robust algorithms, (or) designs, (or) approaches with the capability to accommodate the dynamic behaviors of different learning models. Furthermore, the behavioral/functional improvement of the aforementioned prediction algorithms in terms of different parameters, i.e., performance, accuracy, etc., is essential. As compared to other aforementioned prediction algorithms, the decision tree algorithm (DT) provides higher computational accuracy [11; 18; 19]. Therefore, by considering the robustness, the main objective of this work was to provide performance and an accuracy-oriented classifier using both the decision tree and tabu search algorithms at the same time.