A Chaotic Oppositional Whale Optimisation Algorithm with Firefly Search for Medical Diagnostics
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
A Chaotic Oppositional Whale Optimisation Algorithm with Firefly Search for Medical Diagnostics
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
CMC-Computers, Materials & Continua
ISSN
1546-2218
e-ISSN
1546-2226
Volume of the periodical
72
Issue of the periodical within the volume
1
Country of publishing house
US - UNITED STATES
Number of pages
24
Pages from-to
959-982
UT code for WoS article
000763489500020
EID of the result in the Scopus database
2-s2.0-85125424539