Safe dynamic sparse training of modified RBF networks for joint feature selection and classification-Reference-Cited by-同舟云学术

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Safe dynamic sparse training of modified RBF networks for joint feature selection and classification

Published:2024-10 Issue: Volume:600 Page:128150
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Qian Xusheng^ORCID,Hu Jisu,Zheng Yi,Huang He,Zhou Zhiyong,Dai Yakang

Publisher

Elsevier BV

Reference56 articles.

1. Overview and comparative study of dimensionality reduction techniques for high dimensional data;Ayesha;Inf. Fusion,2020

2. Feature selection based on structured sparsity: A comprehensive study;Gui;IEEE Trans. Neural Netw. Learn. Syst.,2016

3. A comprehensive survey on recent metaheuristics for feature selection;Dokeroglu;Neurocomputing,2022

4. A comprehensive survey on feature selection in the various fields of machine learning;Dhal;Appl. Intell.,2022

5. Feature selection in machine learning: A new perspective;Cai;Neurocomputing,2018

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