Efficient Feature Selection Using Weighted Superposition Attraction Optimization Algorithm

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27230%2F23%3A10252188" target="_blank" >RIV/61989100:27230/23:10252188 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.webofscience.com/wos/woscc/full-record/WOS:000947652200001" target="_blank" >https://www.webofscience.com/wos/woscc/full-record/WOS:000947652200001</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/app13053223" target="_blank" >10.3390/app13053223</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Efficient Feature Selection Using Weighted Superposition Attraction Optimization Algorithm

Popis výsledku v původním jazyce

As the volume of data generated by information systems continues to increase, machine learning (ML) techniques have become essential for the extraction of meaningful insights. However, the sheer volume of data often causes these techniques to become sluggish. To overcome this, feature selection is a vital step in the pre-processing of data. In this paper, we introduce a novel K-nearest neighborhood (KNN)-based wrapper system for feature selection that leverages the iterative improvement ability of the weighted superposition attraction (WSA). We evaluate the performance of WSA against seven well-known metaheuristic algorithms, i.e., differential evolution (DE), genetic algorithm (GA), particle swarm optimization (PSO), flower pollination algorithm (FPA), symbiotic organisms search (SOS), marine predators&apos; algorithm (MPA) and manta ray foraging optimization (MRFO). Our extensive numerical experiments demonstrate that WSA is highly effective for feature selection, achieving a decrease of up to 99% in the number of features for large datasets without sacrificing classification accuracy. In fact, WSA-KNN outperforms traditional ML methods by about 18% and ensemble ML algorithms by 9%. Moreover, WSA-KNN achieves comparable or slightly better solutions when compared with neural networks hybridized with metaheuristics. These findings highlight the importance and potential of WSA for feature selection in modern-day data processing systems.

Název v anglickém jazyce

Efficient Feature Selection Using Weighted Superposition Attraction Optimization Algorithm

Popis výsledku anglicky

As the volume of data generated by information systems continues to increase, machine learning (ML) techniques have become essential for the extraction of meaningful insights. However, the sheer volume of data often causes these techniques to become sluggish. To overcome this, feature selection is a vital step in the pre-processing of data. In this paper, we introduce a novel K-nearest neighborhood (KNN)-based wrapper system for feature selection that leverages the iterative improvement ability of the weighted superposition attraction (WSA). We evaluate the performance of WSA against seven well-known metaheuristic algorithms, i.e., differential evolution (DE), genetic algorithm (GA), particle swarm optimization (PSO), flower pollination algorithm (FPA), symbiotic organisms search (SOS), marine predators&apos; algorithm (MPA) and manta ray foraging optimization (MRFO). Our extensive numerical experiments demonstrate that WSA is highly effective for feature selection, achieving a decrease of up to 99% in the number of features for large datasets without sacrificing classification accuracy. In fact, WSA-KNN outperforms traditional ML methods by about 18% and ensemble ML algorithms by 9%. Moreover, WSA-KNN achieves comparable or slightly better solutions when compared with neural networks hybridized with metaheuristics. These findings highlight the importance and potential of WSA for feature selection in modern-day data processing systems.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20300 - Mechanical engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

Applied Sciences

ISSN

2076-3417

e-ISSN

—

Svazek periodika

13

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

26

Strana od-do

—

Kód UT WoS článku

000947652200001

EID výsledku v databázi Scopus

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