Enhancing classification with hybrid feature selection: A multi-objective genetic algorithm for high-dimensional data-Reference-Cited by-同舟云学术

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Enhancing classification with hybrid feature selection: A multi-objective genetic algorithm for high-dimensional data

Published:2024-12 Issue: Volume:255 Page:124518
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Bohrer Jonas da S.^ORCID,Dorn Márcio^ORCID

Funder

Conselho Nacional de Desenvolvimento Cientifico e Tecnologico

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Fundacao de Amparo a Pesquisa do Estado do Rio Grande do Sul

Publisher

Elsevier BV

Reference59 articles.

1. Feature selection using genetic algorithm for breast cancer diagnosis: Experiment on three different datasets;Aalaei;Iranian Journal of Basic Medical Sciences,2016

2. Robust biomarker identification for cancer diagnosis with ensemble feature selection methods;Abeel;Bioinformatics,2010

3. A GA-based feature selection and parameter optimization of an ANN in diagnosing breast cancer;Ahmad;Pattern Analysis and Applications,2015

4. Support vector machines combined with feature selection for breast cancer diagnosis;Akay;Expert Systems with Applications,2009

5. Feature selection methods on gene expression microarray data for cancer classification: A systematic review;Alhenawi;Computers in Biology and Medicine,2022

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. UniBFS: A novel uniform-solution-driven binary feature selection algorithm for high-dimensional data;Swarm and Evolutionary Computation;2024-12

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