A comparative evaluation of nature-inspired algorithms for feature selection problems

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27230%2F24%3A10253828" target="_blank" >RIV/61989100:27230/24:10253828 - isvavai.cz</a>

Result on the web

<a href="https://www.webofscience.com/wos/woscc/full-record/WOS:001137777000001" target="_blank" >https://www.webofscience.com/wos/woscc/full-record/WOS:001137777000001</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.heliyon.2023.e23571" target="_blank" >10.1016/j.heliyon.2023.e23571</a>

Result language

angličtina

Original language name

A comparative evaluation of nature-inspired algorithms for feature selection problems

Original language description

Feature selection is a critical component of machine learning and data mining which addresses challenges like irrelevance, noise, redundancy in large-scale data etc., which often result in the curse of dimensionality. This study employs a K-nearest neighbour wrapper to implement feature selection using six nature-inspired algorithms, derived from human behaviour and mammalinspired techniques. Evaluated on six real-world datasets, the study aims to compare the performance of these algorithms in terms of accuracy, feature count, fitness, convergence and computational cost. The findings underscore the efficacy of the Human Learning Optimization, Poor and Rich Optimization and Grey Wolf Optimizer algorithms across multiple performance metrics. For instance, for mean fitness, Human Learning Optimization outperforms the others, followed by Poor and Rich Optimization and Harmony Search. The study suggests the potential of human-inspired algorithms, particularly Poor and Rich Optimization, in robust feature selection without compromising classification accuracy.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20300 - Mechanical engineering

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2024

Confidentiality

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

Name of the periodical

Heliyon

ISSN

2405-8440

e-ISSN

2405-8440

Volume of the periodical

10

Issue of the periodical within the volume

1

Country of publishing house

GB - UNITED KINGDOM

Number of pages

19

Pages from-to

—

UT code for WoS article

001137777000001

EID of the result in the Scopus database

—