Performance Improvement of Decision Tree: A Robust Classifier Using Tabu Search Algorithm

被引:11
|
作者
Hafeez, Muhammad Asfand [1 ]
Rashid, Muhammad [2 ]
Tariq, Hassan [1 ]
Ul Abideen, Zain [3 ]
Alotaibi, Saud S. [4 ]
Sinky, Mohammed H. [2 ]
机构
[1] Univ Management & Technol UMT, Sch Engn, Dept Elect Engn, Lahore 5770, Pakistan
[2] Umm Al Qura Univ, Dept Comp Engn, Mecca 21955, Saudi Arabia
[3] Tallinn Univ Technol, Dept Comp Syst, EE-12616 Tallinn, Estonia
[4] Umm Al Qura Univ, Dept Informat Syst, Mecca 21955, Saudi Arabia
来源
APPLIED SCIENCES-BASEL | 2021年 / 11卷 / 15期
关键词
supervised machine learning; decision tree; tabu search; performance improvement; execution time; accuracy; PREDICTION;
D O I
10.3390/app11156728
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
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.
引用
收藏
页数:17
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