IBMRFO: Improved binary manta ray foraging optimization with chaotic tent map and adaptive somersault factor for feature selection-Reference-Cited by-同舟云学术

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IBMRFO: Improved binary manta ray foraging optimization with chaotic tent map and adaptive somersault factor for feature selection

Published:2024-10 Issue: Volume:251 Page:123977
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Zhang Kunpeng^ORCID,Liu Yanheng,Wang Xue,Mei Fang^ORCID,Kang Hui,Sun Geng^ORCID

Publisher

Elsevier BV

Reference66 articles.

1. A multi-objective optimization algorithm for feature selection problems;Abdollahzadeh;Engineering with Computers,2022

2. Manta ray foraging optimization algorithm: Modifications and applications;Abdullahi;IEEE Access,2023

3. Metaheuristic algorithms on feature selection: A survey of one decade of research (2009–2019);Agrawal;IEEE Access,2021

4. Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences;Alber;NPJ Digital Medicine,2019

5. Breast cancer diagnosis using ga feature selection and rotation forest;Aličković;Neural Computing and Applications,2017

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1. IRIME: Mitigating exploitation-exploration imbalance in RIME optimization for feature selection;iScience;2024-08

2. An improved extreme learning machine model for predicting the mechanical property of AZ80 magnesium alloy;Applied Physics A;2024-07-25

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