Feature selection method for banknote dirtiness recognition based on mathematical functions driven slime mould algorithm-Reference-Cited by-同舟云学术

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Feature selection method for banknote dirtiness recognition based on mathematical functions driven slime mould algorithm

Published:2024-10 Issue: Volume:252 Page:124262
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Guo Fu-Jun,Sun Wei-Zhong,Wang Jie-Sheng^ORCID,Zhang Min,Hou Jia-Ning,Zhu Jun-Hua,Bao Yin-Yin

Publisher

Elsevier BV

Reference50 articles.

1. Automatic selection of heavy-tailed distributions-based synergy Henry gas solubility and Harris hawk optimizer for feature selection: Case study drug design and discovery;Abd Elaziz;Artificial Intelligence Review,2021

2. A new fusion of grey wolf optimizer algorithm with a two-phase mutation for feature selection;Abdel-Basset;Expert Systems with Applications,2020

3. An efficient binary slime mould algorithm integrated with a novel attacking-feeding strategy for feature selection;Abdel-Basset;Computers & Industrial Engineering,2021

4. Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering;Abualigah;The Journal of Supercomputing,2017

5. Improving grasshopper optimization algorithm for hyperparameters estimation and feature selection in support vector regression;Algamal;Chemometrics and Intelligent Laboratory Systems,2021

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