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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

HELIYON

ISSN

2405-8440

e-ISSN

2405-8440

Svazek periodika

9

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

11

Strana od-do

"Article Number: e15378"

Kód UT WoS článku

000998679400001

EID výsledku v databázi Scopus

2-s2.0-85152116903