Breast Cancer Diagnosis Using a Novel Parallel Support Vector Machine with Harris Hawks Optimization
Three contributions are proposed. Firstly, a novel hybrid classifier (HHO-SVM) is introduced, which is a combination between the Harris hawks optimization (HHO) and a support vector machine (SVM) is introduced. Second, the performance of the HHO-SVM is enhanced using the conventional normalization m...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/14/3251 |
| _version_ | 1797588381065019392 |
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| author | Sultan Almotairi Elsayed Badr Mustafa Abdul Salam Hagar Ahmed |
| author_facet | Sultan Almotairi Elsayed Badr Mustafa Abdul Salam Hagar Ahmed |
| author_sort | Sultan Almotairi |
| collection | DOAJ |
| description | Three contributions are proposed. Firstly, a novel hybrid classifier (HHO-SVM) is introduced, which is a combination between the Harris hawks optimization (HHO) and a support vector machine (SVM) is introduced. Second, the performance of the HHO-SVM is enhanced using the conventional normalization method. The final contribution is to improve the efficiency of the HHO-SVM by adopting a parallel approach that employs the data distribution. The proposed models are evaluated using the Wisconsin Diagnosis Breast Cancer (WDBC) dataset. The results show that the HHO-SVM achieves a 98.24% accuracy rate with the normalization scaling technique, outperforming other related works. On the other hand, the HHO-SVM achieves a 99.47% accuracy rate with the equilibration scaling technique, which is better than other previous works. Finally, to compare the three effective scaling strategies on four CPU cores, the parallel version of the proposed model provides an acceleration of 3.97. |
| first_indexed | 2024-03-11T00:52:07Z |
| format | Article |
| id | doaj.art-c51c0b944e534b2da8defd1348e3a39b |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-11T00:52:07Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-c51c0b944e534b2da8defd1348e3a39b2023-11-18T20:22:49ZengMDPI AGMathematics2227-73902023-07-011114325110.3390/math11143251Breast Cancer Diagnosis Using a Novel Parallel Support Vector Machine with Harris Hawks OptimizationSultan Almotairi0Elsayed Badr1Mustafa Abdul Salam2Hagar Ahmed3Department of Computer Science, College of Computer and Information Sciences, Majmaah University, Al-Majmaah 11952, Saudi ArabiaScientific Computing Department, Faculty of Computers and Artificial Intelligence, Benha University, Benha 13511, EgyptArtificial Intelligence Department, Faculty of Computers and Artificial Intelligence, Benha University, Benha 13511, EgyptScientific Computing Department, Faculty of Computers and Artificial Intelligence, Benha University, Benha 13511, EgyptThree contributions are proposed. Firstly, a novel hybrid classifier (HHO-SVM) is introduced, which is a combination between the Harris hawks optimization (HHO) and a support vector machine (SVM) is introduced. Second, the performance of the HHO-SVM is enhanced using the conventional normalization method. The final contribution is to improve the efficiency of the HHO-SVM by adopting a parallel approach that employs the data distribution. The proposed models are evaluated using the Wisconsin Diagnosis Breast Cancer (WDBC) dataset. The results show that the HHO-SVM achieves a 98.24% accuracy rate with the normalization scaling technique, outperforming other related works. On the other hand, the HHO-SVM achieves a 99.47% accuracy rate with the equilibration scaling technique, which is better than other previous works. Finally, to compare the three effective scaling strategies on four CPU cores, the parallel version of the proposed model provides an acceleration of 3.97.https://www.mdpi.com/2227-7390/11/14/3251support vector machineHarris hawks optimizationscaling techniquesparallel processing |
| spellingShingle | Sultan Almotairi Elsayed Badr Mustafa Abdul Salam Hagar Ahmed Breast Cancer Diagnosis Using a Novel Parallel Support Vector Machine with Harris Hawks Optimization Mathematics support vector machine Harris hawks optimization scaling techniques parallel processing |
| title | Breast Cancer Diagnosis Using a Novel Parallel Support Vector Machine with Harris Hawks Optimization |
| title_full | Breast Cancer Diagnosis Using a Novel Parallel Support Vector Machine with Harris Hawks Optimization |
| title_fullStr | Breast Cancer Diagnosis Using a Novel Parallel Support Vector Machine with Harris Hawks Optimization |
| title_full_unstemmed | Breast Cancer Diagnosis Using a Novel Parallel Support Vector Machine with Harris Hawks Optimization |
| title_short | Breast Cancer Diagnosis Using a Novel Parallel Support Vector Machine with Harris Hawks Optimization |
| title_sort | breast cancer diagnosis using a novel parallel support vector machine with harris hawks optimization |
| topic | support vector machine Harris hawks optimization scaling techniques parallel processing |
| url | https://www.mdpi.com/2227-7390/11/14/3251 |
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