Araştırma Makalesi

Evaluation of Performance of Feature Selection of Meta-Heuristic Optimization Methods in Medical Data

Cilt: IDAP-2023 : International Artificial Intelligence and Data Processing Symposium Sayı: IDAP-2023 18 Ekim 2023
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Evaluation of Performance of Feature Selection of Meta-Heuristic Optimization Methods in Medical Data

Öz

In knowledge discovery, the processes of applying data cleaning, data integration, data selection-transformation, and data mining methods and obtaining meaningful information from the obtained patterns are performed, respectively. In recent years, it has become quite common to use metaheuristic optimization methods in the data selection phase. In this study, the nearest neighbor algorithm, support vector machine, and decision tree algorithms from machine learning algorithms were used on health data obtained from the University of California, Irvine. The whale optimization algorithm, salp swarm optimization, slime mould optimization, particle swarm optimization, and Harris Hawks optimization methods were used for feature selection. The obtained results were compared in detail.

Anahtar Kelimeler

Kaynakça

  1. Abualigah, L., Shehab, M., Alshinwan, M., & Alabool, H. (2020). Salp swarm algorithm: a comprehensive survey. Neural Computing and Applications, 32(15), 11195–11215.
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  3. Altay, O., & Varol Altay, E. (2022). Investigation of Slime Mould Algorithm and Hybrid Slime Mould Algorithms’ Performance in Global Optimization Problems. DÜMF Mühendislik Dergisi, 4, 661–671.
  4. Baştanlar, Y., & Ozuysal, M. (2014). Introduction to Machine Learning Second Edition. In Methods in molecular biology (Clifton, N.J.) (Vol. 1107).
  5. Brereton, R. G., & Lloyd, G. R. (2010). Support Vector Machines for classification and regression. Analyst, 135(2), 230–267.
  6. Castellanos-garzón, J. A., Costa, E., Luis, J., Jaimes, S., & Corchado, J. M. (2019). Knowledge-Based Systems An evolutionary framework for machine learning applied to medical. Knowledge-Based Systems, 185, 104982.
  7. Cervantes, J., Garcia-Lamont, F., Rodríguez-Mazahua, L., & Lopez, A. (2020). A comprehensive survey on support vector machine classification: Applications, challenges and trends. Neurocomputing, 408, 189–215.
  8. Chen, H., Li, C., Mafarja, M., Heidari, A. A., Chen, Y., & Cai, Z. (2023). Slime mould algorithm: a comprehensive review of recent variants and applications. International Journal of Systems Science, 54(1), 204–235. Chicco, D., & Jurman, G. (2020). Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone. BMC Medical Informatics and Decision Making, 20(1), 1–16. Dokeroglu, T., Deniz, A., & Kiziloz, H. E. (2022). A comprehensive survey on recent metaheuristics for feature selection. Neurocomputing, 494, 269–296.

Ayrıntılar

Birincil Dil

İngilizce

Konular

Veri Madenciliği ve Bilgi Keşfi

Bölüm

Araştırma Makalesi

Yayımlanma Tarihi

18 Ekim 2023

Gönderilme Tarihi

29 Ağustos 2023

Kabul Tarihi

16 Ekim 2023

Yayımlandığı Sayı

Yıl 2023 Cilt: IDAP-2023 : International Artificial Intelligence and Data Processing Symposium Sayı: IDAP-2023

Kaynak Göster

APA
Gündoğdu, H., & Altay, O. (2023). Evaluation of Performance of Feature Selection of Meta-Heuristic Optimization Methods in Medical Data. Computer Science, IDAP-2023 : International Artificial Intelligence and Data Processing Symposium(IDAP-2023), 58-66. https://doi.org/10.53070/bbd.1351629
AMA
1.Gündoğdu H, Altay O. Evaluation of Performance of Feature Selection of Meta-Heuristic Optimization Methods in Medical Data. JCS. 2023;IDAP-2023 : International Artificial Intelligence and Data Processing Symposium(IDAP-2023):58-66. doi:10.53070/bbd.1351629
Chicago
Gündoğdu, Hüseyin, ve Osman Altay. 2023. “Evaluation of Performance of Feature Selection of Meta-Heuristic Optimization Methods in Medical Data”. Computer Science IDAP-2023 : International Artificial Intelligence and Data Processing Symposium (IDAP-2023): 58-66. https://doi.org/10.53070/bbd.1351629.
EndNote
Gündoğdu H, Altay O (01 Ekim 2023) Evaluation of Performance of Feature Selection of Meta-Heuristic Optimization Methods in Medical Data. Computer Science IDAP-2023 : International Artificial Intelligence and Data Processing Symposium IDAP-2023 58–66.
IEEE
[1]H. Gündoğdu ve O. Altay, “Evaluation of Performance of Feature Selection of Meta-Heuristic Optimization Methods in Medical Data”, JCS, c. IDAP-2023 : International Artificial Intelligence and Data Processing Symposium, sy IDAP-2023, ss. 58–66, Eki. 2023, doi: 10.53070/bbd.1351629.
ISNAD
Gündoğdu, Hüseyin - Altay, Osman. “Evaluation of Performance of Feature Selection of Meta-Heuristic Optimization Methods in Medical Data”. Computer Science IDAP-2023 : INTERNATIONAL ARTIFICIAL INTELLIGENCE AND DATA PROCESSING SYMPOSIUM/IDAP-2023 (01 Ekim 2023): 58-66. https://doi.org/10.53070/bbd.1351629.
JAMA
1.Gündoğdu H, Altay O. Evaluation of Performance of Feature Selection of Meta-Heuristic Optimization Methods in Medical Data. JCS. 2023;IDAP-2023 : International Artificial Intelligence and Data Processing Symposium:58–66.
MLA
Gündoğdu, Hüseyin, ve Osman Altay. “Evaluation of Performance of Feature Selection of Meta-Heuristic Optimization Methods in Medical Data”. Computer Science, c. IDAP-2023 : International Artificial Intelligence and Data Processing Symposium, sy IDAP-2023, Ekim 2023, ss. 58-66, doi:10.53070/bbd.1351629.
Vancouver
1.Hüseyin Gündoğdu, Osman Altay. Evaluation of Performance of Feature Selection of Meta-Heuristic Optimization Methods in Medical Data. JCS. 01 Ekim 2023;IDAP-2023 : International Artificial Intelligence and Data Processing Symposium(IDAP-2023):58-66. doi:10.53070/bbd.1351629

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