Cargando…

Feature Selection on Elite Hybrid Binary Cuckoo Search in Binary Label Classification

For the low optimization accuracy of the cuckoo search algorithm, a new search algorithm, the Elite Hybrid Binary Cuckoo Search (EHBCS) algorithm, is improved by feature weighting and elite strategy. The EHBCS algorithm has been designed for feature selection on a series of binary classification dat...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhao, Maoxian, Qin, Yue
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8133872/
https://www.ncbi.nlm.nih.gov/pubmed/34055039
http://dx.doi.org/10.1155/2021/5588385
_version_ 1783695138132852736
author Zhao, Maoxian
Qin, Yue
author_facet Zhao, Maoxian
Qin, Yue
author_sort Zhao, Maoxian
collection PubMed
description For the low optimization accuracy of the cuckoo search algorithm, a new search algorithm, the Elite Hybrid Binary Cuckoo Search (EHBCS) algorithm, is improved by feature weighting and elite strategy. The EHBCS algorithm has been designed for feature selection on a series of binary classification datasets, including low-dimensional and high-dimensional samples by SVM classifier. The experimental results show that the EHBCS algorithm achieves better classification performances compared with binary genetic algorithm and binary particle swarm optimization algorithm. Besides, we explain its superiority in terms of standard deviation, sensitivity, specificity, precision, and F-measure.
format Online
Article
Text
id pubmed-8133872
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Hindawi
record_format MEDLINE/PubMed
spelling pubmed-81338722021-05-27 Feature Selection on Elite Hybrid Binary Cuckoo Search in Binary Label Classification Zhao, Maoxian Qin, Yue Comput Math Methods Med Research Article For the low optimization accuracy of the cuckoo search algorithm, a new search algorithm, the Elite Hybrid Binary Cuckoo Search (EHBCS) algorithm, is improved by feature weighting and elite strategy. The EHBCS algorithm has been designed for feature selection on a series of binary classification datasets, including low-dimensional and high-dimensional samples by SVM classifier. The experimental results show that the EHBCS algorithm achieves better classification performances compared with binary genetic algorithm and binary particle swarm optimization algorithm. Besides, we explain its superiority in terms of standard deviation, sensitivity, specificity, precision, and F-measure. Hindawi 2021-05-11 /pmc/articles/PMC8133872/ /pubmed/34055039 http://dx.doi.org/10.1155/2021/5588385 Text en Copyright © 2021 Maoxian Zhao and Yue Qin. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Zhao, Maoxian
Qin, Yue
Feature Selection on Elite Hybrid Binary Cuckoo Search in Binary Label Classification
title Feature Selection on Elite Hybrid Binary Cuckoo Search in Binary Label Classification
title_full Feature Selection on Elite Hybrid Binary Cuckoo Search in Binary Label Classification
title_fullStr Feature Selection on Elite Hybrid Binary Cuckoo Search in Binary Label Classification
title_full_unstemmed Feature Selection on Elite Hybrid Binary Cuckoo Search in Binary Label Classification
title_short Feature Selection on Elite Hybrid Binary Cuckoo Search in Binary Label Classification
title_sort feature selection on elite hybrid binary cuckoo search in binary label classification
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8133872/
https://www.ncbi.nlm.nih.gov/pubmed/34055039
http://dx.doi.org/10.1155/2021/5588385
work_keys_str_mv AT zhaomaoxian featureselectiononelitehybridbinarycuckoosearchinbinarylabelclassification
AT qinyue featureselectiononelitehybridbinarycuckoosearchinbinarylabelclassification