Quasi-reflection learning arithmetic optimization algorithm firefly search for feature selection

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F23%3A50020595" target="_blank" >RIV/62690094:18470/23:50020595 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S2405844023025859?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2405844023025859?via%3Dihub</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.heliyon.2023.e15378" target="_blank" >10.1016/j.heliyon.2023.e15378</a>

Result language
angličtina
Original language name
Quasi-reflection learning arithmetic optimization algorithm firefly search for feature selection
Original language description
With the whirlwind evolution of technology, the quantity of stored data within datasets is rapidly expanding. As a result, extracting crucial and relevant information from said datasets is a gruelling task. Feature selection is a critical preprocessing task for machine learning to reduce the excess data in a set. This research presents a novel quasi-reflection learning arithmetic optimization algorithm -firefly search, an enhanced version of the original arithmetic optimization algorithm. Quasi-reflection learning mechanism was implemented for enhancement of population diversity, while firefly algorithm metaheuristics were used to improve the exploitation abilities of the original arithmetic optimization algorithm. The aim of this wrapper-based method is to tackle a specific classification problem by selecting an optimal feature subset. The proposed algorithm is tested and compared with various well-known methods on ten unconstrained benchmark functions, then on twenty-one standard datasets gathered from the University of California, Irvine Repository and Arizona State University. Additionally, the proposed approach is applied to the Corona disease dataset. The experimental results verify the improvements of the presented method and their statistical significance.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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