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A modified fuzzy K-nearest neighbor using sine cosine algorithm for two-classes and multi-classes datasets
The sine and cosine algorithm has become a widely researched swarm optimization method in recent years due to its simplicity and effectiveness. Based on the advantages, the study in this paper delves deeper into the key parameters that influence the performance of the algorithm, and has implemented...
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creator | Zheng, Chengfeng Kasihmuddin, Mohd Shareduwan Mohd Mansor, Mohd. Asyraf Jamaludin, Siti Zulaikha Mohd Zamri, Nur Ezlin |
description | The sine and cosine algorithm has become a widely researched swarm optimization method in recent years due to its simplicity and effectiveness. Based on the advantages, the study in this paper delves deeper into the key parameters that influence the performance of the algorithm, and has implemented modifications such as integrating the reverse learning algorithm and adding elite opposition solution to create the modified Sine and Cosine Algorithm (the modified SCA). Furthermore, by combining the fuzzy k-nearest neighbor method with the modified SCA, the study simulates numeric datasets with two or multiple classes, and analyzes the results. The accuracy rate (ACC) achieved by the modified SCA FKNN in this paper is compared to other models, with data comparison results and tables presented for each. The modified SCA FKNN proposed in this paper has obvious advantages on accuracy rate(ACC). |
doi_str_mv | 10.1063/5.0192167 |
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Asyraf ; Jamaludin, Siti Zulaikha Mohd ; Zamri, Nur Ezlin</creator><contributor>Hamid, Mohd Rashid Ab ; Satari, Siti Zanariah ; Nasir, Nadirah Mohd ; Yusof, Yuhani ; Jusoh, Rahimah</contributor><creatorcontrib>Zheng, Chengfeng ; Kasihmuddin, Mohd Shareduwan Mohd ; Mansor, Mohd. Asyraf ; Jamaludin, Siti Zulaikha Mohd ; Zamri, Nur Ezlin ; Hamid, Mohd Rashid Ab ; Satari, Siti Zanariah ; Nasir, Nadirah Mohd ; Yusof, Yuhani ; Jusoh, Rahimah</creatorcontrib><description>The sine and cosine algorithm has become a widely researched swarm optimization method in recent years due to its simplicity and effectiveness. Based on the advantages, the study in this paper delves deeper into the key parameters that influence the performance of the algorithm, and has implemented modifications such as integrating the reverse learning algorithm and adding elite opposition solution to create the modified Sine and Cosine Algorithm (the modified SCA). Furthermore, by combining the fuzzy k-nearest neighbor method with the modified SCA, the study simulates numeric datasets with two or multiple classes, and analyzes the results. The accuracy rate (ACC) achieved by the modified SCA FKNN in this paper is compared to other models, with data comparison results and tables presented for each. The modified SCA FKNN proposed in this paper has obvious advantages on accuracy rate(ACC).</description><identifier>ISSN: 0094-243X</identifier><identifier>EISSN: 1551-7616</identifier><identifier>DOI: 10.1063/5.0192167</identifier><identifier>CODEN: APCPCS</identifier><language>eng</language><publisher>Melville: American Institute of Physics</publisher><subject>Algorithms ; Datasets ; Machine learning ; Parameter modification ; Trigonometric functions</subject><ispartof>AIP Conference Proceedings, 2024, Vol.2895 (1)</ispartof><rights>Author(s)</rights><rights>2024 Author(s). 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source | American Institute of Physics:Jisc Collections:Transitional Journals Agreement 2021-23 (Reading list) |
subjects | Algorithms Datasets Machine learning Parameter modification Trigonometric functions |
title | A modified fuzzy K-nearest neighbor using sine cosine algorithm for two-classes and multi-classes datasets |
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