Q-learning guided mutational Harris hawk optimizer for high-dimensional gene data feature selection-Reference-Cited by-同舟云学术

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Q-learning guided mutational Harris hawk optimizer for high-dimensional gene data feature selection

Published:2024-08 Issue: Volume:161 Page:111734
ISSN:1568-4946
Container-title:Applied Soft Computing
language:en
Short-container-title:Applied Soft Computing

Author:

Peng Lemin^ORCID,Li Xinru,Yu Liang^ORCID,Heidari Ali Asghar,Chen Huiling,Liang Guoxi

Funder

National Natural Science Foundation of China

Natural Science Foundation of Zhejiang Province

Publisher

Elsevier BV

Reference103 articles.

1. Deep fuzzy neural networks for biomarker selection for accurate cancer detection;Mudiyanselage;IEEE Trans. Fuzzy Syst.,2020

2. A comprehensive survey of deep learning techniques in protein function prediction;Dhanuka;IEEE-ACM Trans. Comput. Biol. Bioinforma.,2023

3. A hybrid two-stage teaching-learning-based optimization algorithm for feature selection in bioinformatics;Kang;IEEE-ACM Trans. Comput. Biol. Bioinforma.,2023

4. Lightweight transformer image feature extraction network;Zheng;PeerJ Comput. Sci.,2024

5. SLNL: a novel method for gene selection and phenotype classification;Huang;Int. J. Intell. Syst.,2022

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

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