A Comparative Study of Bird-Based Metaphor Algorithms for Feature Selection Problems

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27230%2F25%3A10259139" target="_blank" >RIV/61989100:27230/25:10259139 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-031-78440-8_8" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-78440-8_8</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-78440-8_8" target="_blank" >10.1007/978-3-031-78440-8_8</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A Comparative Study of Bird-Based Metaphor Algorithms for Feature Selection Problems

Popis výsledku v původním jazyce

Data must be interpreted correctly to understand the relevant information it carries. Especially today, data has massive content that needs to be retrieved by a specialist to be evaluated and validated before deciding on a solution. These massive data are commonly executed by computers trained through various specialized algorithms. Feature selection (FS) is critical in modern machine learning frameworks. Metaheuristic techniques can efficiently carry out FS to reduce the data dimension. In this paper, feature selection is carried out by using a K-Nearest Neighbors (KNN) wrapper with bird-based metaphor algorithms. Five different bird-based optimizers, namely, Cuckoo Search, Harris Hawks, Crow Search, Stain Bowerbird and Emperor Penguin, are considered for the study. For analyzing the different algorithms, six different types of datasets are used. The performance of an average number of features selected (AFS), accuracy, fitness, convergence capabilities and computational cost is compared. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Název v anglickém jazyce

A Comparative Study of Bird-Based Metaphor Algorithms for Feature Selection Problems

Popis výsledku anglicky

Data must be interpreted correctly to understand the relevant information it carries. Especially today, data has massive content that needs to be retrieved by a specialist to be evaluated and validated before deciding on a solution. These massive data are commonly executed by computers trained through various specialized algorithms. Feature selection (FS) is critical in modern machine learning frameworks. Metaheuristic techniques can efficiently carry out FS to reduce the data dimension. In this paper, feature selection is carried out by using a K-Nearest Neighbors (KNN) wrapper with bird-based metaphor algorithms. Five different bird-based optimizers, namely, Cuckoo Search, Harris Hawks, Crow Search, Stain Bowerbird and Emperor Penguin, are considered for the study. For analyzing the different algorithms, six different types of datasets are used. The performance of an average number of features selected (AFS), accuracy, fitness, convergence capabilities and computational cost is compared. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Druh

C - Kapitola v odborné knize

CEP obor

—

OECD FORD obor

20301 - Mechanical engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název knihy nebo sborníku

Studies in Computational Intelligence

ISBN

978-3-031-78439-2

Počet stran výsledku

21

Strana od-do

1-806

Počet stran knihy

806

Název nakladatele

Springer Science and Business Media Deutschland GmbH

Místo vydání

Cham, Switzerland

Kód UT WoS kapitoly

—