Hybrid binary whale with harris hawks for feature selection
A tremendous flow of big data has come from the growing use of digital technology and intelligent systems. This has resulted in an increase in not just the dimensional issues that big data encounters, but also the number of challenges that big data faces, including redundancies and useless features....
| Main Authors: | Alwajih, R., Abdulkadir, S.J., Al Hussian, H., Aziz, N., Al-Tashi, Q., Mirjalili, S., Alqushaibi, A. |
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| Institution: | Universiti Teknologi Petronas |
| Record Id / ISBN-0: | utp-eprints.33390 / |
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Springer Science and Business Media Deutschland GmbH
2022
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https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133560377&doi=10.1007%2fs00521-022-07522-9&partnerID=40&md5=47c05474fba4047060b288e8c21ae1a9 http://eprints.utp.edu.my/33390/ |
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utp-eprints.333902022-07-26T08:45:37Z Hybrid binary whale with harris hawks for feature selection Alwajih, R. Abdulkadir, S.J. Al Hussian, H. Aziz, N. Al-Tashi, Q. Mirjalili, S. Alqushaibi, A. A tremendous flow of big data has come from the growing use of digital technology and intelligent systems. This has resulted in an increase in not just the dimensional issues that big data encounters, but also the number of challenges that big data faces, including redundancies and useless features. As a result, feature selection is offered as a method for eliminating unwanted characteristics. This study introduces the BWOAHHO memetic technique, which combines the binary hybrid Whale Optimization Algorithm (WOA) with Harris Hawks Optimization (HHO). A transfer function to transfer continuous characteristics to binary to fulfill the feature selection nature condition. The efficiency of the selected attributes is assessed using a wrapper k-Nearest neighbor (KNN) Classifier. About 18 benchmark datasets obtained from UCI repository were utilized to measure the proposed method�s proficiency. The performance of the novel hybrid technique was evaluated by comparing to that of WOA, HHO, Particle Swarm Optimization (PSO), the Genetic Algorithm (GA), and the WOASAT-2. With the new hybrid feature selection method, the WOA algorithm�s efficiency was improved. Classification accuracy, average fitness, average selected attributes, and computational time were all used as performance indicators. In terms of accuracy, the proposed BWOHHO algorithm compared with 5 similar metaheuristic algorithms. The BWOAHHO had a classification accuracy of 92 in the 18 datasets, which was higher than BWOA (90), BPSO (82), and BGA (82). (83), the fitness measures of the BWOHHO algorithm are 0.08, which is lower than the average fitness of BWOA, BPSO, and BGA., in terms of selected attribute size compare the proposed BWOAHHO algorithm to the results obtained by the other five techniques The average selected feature sizes for BWOAHHO, BWOA, BHHO, BGA, and WOASAT-2 were 18.07, 20.12, 22.3, 22.32, 22.40, and 15.99, respectively, and computing time for the proposed BWOHHO was 7.36 in second which was the lowest computed value. To determine the significance of BWOAHHO, a statistical one-way ANOVA test was used. When compared to existing algorithms, the proposed approach produced better results. © 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature. Springer Science and Business Media Deutschland GmbH 2022 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133560377&doi=10.1007%2fs00521-022-07522-9&partnerID=40&md5=47c05474fba4047060b288e8c21ae1a9 Alwajih, R. and Abdulkadir, S.J. and Al Hussian, H. and Aziz, N. and Al-Tashi, Q. and Mirjalili, S. and Alqushaibi, A. (2022) Hybrid binary whale with harris hawks for feature selection. Neural Computing and Applications . http://eprints.utp.edu.my/33390/ |
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Universiti Teknologi Petronas |
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| description |
A tremendous flow of big data has come from the growing use of digital technology and intelligent systems. This has resulted in an increase in not just the dimensional issues that big data encounters, but also the number of challenges that big data faces, including redundancies and useless features. As a result, feature selection is offered as a method for eliminating unwanted characteristics. This study introduces the BWOAHHO memetic technique, which combines the binary hybrid Whale Optimization Algorithm (WOA) with Harris Hawks Optimization (HHO). A transfer function to transfer continuous characteristics to binary to fulfill the feature selection nature condition. The efficiency of the selected attributes is assessed using a wrapper k-Nearest neighbor (KNN) Classifier. About 18 benchmark datasets obtained from UCI repository were utilized to measure the proposed method�s proficiency. The performance of the novel hybrid technique was evaluated by comparing to that of WOA, HHO, Particle Swarm Optimization (PSO), the Genetic Algorithm (GA), and the WOASAT-2. With the new hybrid feature selection method, the WOA algorithm�s efficiency was improved. Classification accuracy, average fitness, average selected attributes, and computational time were all used as performance indicators. In terms of accuracy, the proposed BWOHHO algorithm compared with 5 similar metaheuristic algorithms. The BWOAHHO had a classification accuracy of 92 in the 18 datasets, which was higher than BWOA (90), BPSO (82), and BGA (82). (83), the fitness measures of the BWOHHO algorithm are 0.08, which is lower than the average fitness of BWOA, BPSO, and BGA., in terms of selected attribute size compare the proposed BWOAHHO algorithm to the results obtained by the other five techniques The average selected feature sizes for BWOAHHO, BWOA, BHHO, BGA, and WOASAT-2 were 18.07, 20.12, 22.3, 22.32, 22.40, and 15.99, respectively, and computing time for the proposed BWOHHO was 7.36 in second which was the lowest computed value. To determine the significance of BWOAHHO, a statistical one-way ANOVA test was used. When compared to existing algorithms, the proposed approach produced better results. © 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature. |
| format |
Article |
| author |
Alwajih, R. Abdulkadir, S.J. Al Hussian, H. Aziz, N. Al-Tashi, Q. Mirjalili, S. Alqushaibi, A. |
| spellingShingle |
Alwajih, R. Abdulkadir, S.J. Al Hussian, H. Aziz, N. Al-Tashi, Q. Mirjalili, S. Alqushaibi, A. Hybrid binary whale with harris hawks for feature selection |
| author_sort |
Alwajih, R. |
| title |
Hybrid binary whale with harris hawks for feature selection |
| title_short |
Hybrid binary whale with harris hawks for feature selection |
| title_full |
Hybrid binary whale with harris hawks for feature selection |
| title_fullStr |
Hybrid binary whale with harris hawks for feature selection |
| title_full_unstemmed |
Hybrid binary whale with harris hawks for feature selection |
| title_sort |
hybrid binary whale with harris hawks for feature selection |
| publisher |
Springer Science and Business Media Deutschland GmbH |
| publishDate |
2022 |
| url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133560377&doi=10.1007%2fs00521-022-07522-9&partnerID=40&md5=47c05474fba4047060b288e8c21ae1a9 http://eprints.utp.edu.my/33390/ |
| _version_ |
1741197846051291136 |
| score |
11.62408 |