A Chaotic Oppositional Whale Optimisation Algorithm with Firefly Search for Medical Diagnostics

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F22%3A50019400" target="_blank" >RIV/62690094:18470/22:50019400 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.techscience.com/cmc/v72n1/46919" target="_blank" >https://www.techscience.com/cmc/v72n1/46919</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.32604/cmc.2022.024989" target="_blank" >10.32604/cmc.2022.024989</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A Chaotic Oppositional Whale Optimisation Algorithm with Firefly Search for Medical Diagnostics

Popis výsledku v původním jazyce

There is a growing interest in the study development of artificial intelligence and machine learning, especially regarding the support vector machine pattern classification method. This study proposes an enhanced implementation of the well-known whale optimisation algorithm, which combines chaotic and opposition-based learning strategies, which is adopted for hyper-parameter optimisation and feature selection machine learning challenges. The whale optimisation algorithm is a relatively recent addition to the group of swarm intelligence algorithms commonly used for optimisation. The Proposed improved whale optimisation algorithm was first tested for standard unconstrained CEC2017 benchmark suite and it was later adapted for simultaneous feature selection and support vector machine hyper-parameter tuning and validated for medical diagnostics by using breast cancer, diabetes, and erythemato-squamous dataset. The performance of the proposed model is compared with multiple competitive support vector machine models boosted with other metaheuristics, including another improved whale optimisation sation algorithms, and genetic algorithms. Results of the simulation show that the proposed model outperforms other competitors concerning the performance of classification and the selected subset feature size.

Název v anglickém jazyce

A Chaotic Oppositional Whale Optimisation Algorithm with Firefly Search for Medical Diagnostics

Popis výsledku anglicky

There is a growing interest in the study development of artificial intelligence and machine learning, especially regarding the support vector machine pattern classification method. This study proposes an enhanced implementation of the well-known whale optimisation algorithm, which combines chaotic and opposition-based learning strategies, which is adopted for hyper-parameter optimisation and feature selection machine learning challenges. The whale optimisation algorithm is a relatively recent addition to the group of swarm intelligence algorithms commonly used for optimisation. The Proposed improved whale optimisation algorithm was first tested for standard unconstrained CEC2017 benchmark suite and it was later adapted for simultaneous feature selection and support vector machine hyper-parameter tuning and validated for medical diagnostics by using breast cancer, diabetes, and erythemato-squamous dataset. The performance of the proposed model is compared with multiple competitive support vector machine models boosted with other metaheuristics, including another improved whale optimisation sation algorithms, and genetic algorithms. Results of the simulation show that the proposed model outperforms other competitors concerning the performance of classification and the selected subset feature size.

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

2022

Kód důvěrnosti údajů

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

Název periodika

CMC-Computers, Materials &amp; Continua

ISSN

1546-2218

e-ISSN

1546-2226

Svazek periodika

72

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

24

Strana od-do

959-982

Kód UT WoS článku

000763489500020

EID výsledku v databázi Scopus

2-s2.0-85125424539