Performance Improvement of Decision Tree: A Robust Classifier Using Tabu Search Algorithm
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 evolv...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/15/6728 |
| _version_ | 1797525859930734592 |
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| author | Muhammad Asfand Hafeez Muhammad Rashid Hassan Tariq Zain Ul Abideen Saud S. Alotaibi Mohammed H. Sinky |
| author_facet | Muhammad Asfand Hafeez Muhammad Rashid Hassan Tariq Zain Ul Abideen Saud S. Alotaibi Mohammed H. Sinky |
| author_sort | Muhammad Asfand Hafeez |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T09:19:59Z |
| format | Article |
| id | doaj.art-96276d013b5f4cc9a67628d28cee9260 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T09:19:59Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-96276d013b5f4cc9a67628d28cee92602023-11-22T05:18:35ZengMDPI AGApplied Sciences2076-34172021-07-011115672810.3390/app11156728Performance Improvement of Decision Tree: A Robust Classifier Using Tabu Search AlgorithmMuhammad Asfand Hafeez0Muhammad Rashid1Hassan Tariq2Zain Ul Abideen3Saud S. Alotaibi4Mohammed H. Sinky5Department of Electrical Engineering, School of Engineering, University of Management and Technology (UMT), Lahore 5770, PakistanDepartment of Computer Engineering, Umm Al-Qura University, Makkah 21955, Saudi ArabiaDepartment of Electrical Engineering, School of Engineering, University of Management and Technology (UMT), Lahore 5770, PakistanDepartment of Computer Systems, Tallinn University of Technology, Tallinn 12616, EstoniaDepartment of Information Systems, Umm Al-Qura University, Makkah 21955, Saudi ArabiaDepartment of Computer Engineering, Umm Al-Qura University, Makkah 21955, Saudi ArabiaClassification 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.https://www.mdpi.com/2076-3417/11/15/6728supervised machine learningdecision treetabu searchperformance improvementexecution timeaccuracy |
| spellingShingle | Muhammad Asfand Hafeez Muhammad Rashid Hassan Tariq Zain Ul Abideen Saud S. Alotaibi Mohammed H. Sinky Performance Improvement of Decision Tree: A Robust Classifier Using Tabu Search Algorithm Applied Sciences supervised machine learning decision tree tabu search performance improvement execution time accuracy |
| title | Performance Improvement of Decision Tree: A Robust Classifier Using Tabu Search Algorithm |
| title_full | Performance Improvement of Decision Tree: A Robust Classifier Using Tabu Search Algorithm |
| title_fullStr | Performance Improvement of Decision Tree: A Robust Classifier Using Tabu Search Algorithm |
| title_full_unstemmed | Performance Improvement of Decision Tree: A Robust Classifier Using Tabu Search Algorithm |
| title_short | Performance Improvement of Decision Tree: A Robust Classifier Using Tabu Search Algorithm |
| title_sort | performance improvement of decision tree a robust classifier using tabu search algorithm |
| topic | supervised machine learning decision tree tabu search performance improvement execution time accuracy |
| url | https://www.mdpi.com/2076-3417/11/15/6728 |
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