Feature Selection for High-Dimensional Datasets through a Novel Artificial Bee Colony Framework

There are generally many redundant and irrelevant features in high-dimensional datasets, which leads to the decline of classification performance and the extension of execution time. To tackle this problem, feature selection techniques are used to screen out redundant and irrelevant features. The ar...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Yuanzi Zhang, Jing Wang, Xiaolin Li, Shiguo Huang, Xiuli Wang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	artificial bee colony algorithm high dimensionality feature selection exploration–exploitation balance Industrial engineering. Management engineering T55.4-60.8 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/4684a31a5083410dacbff9c122c956ce
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:4684a31a5083410dacbff9c122c956ce
record_format	dspace
spelling	oai:doaj.org-article:4684a31a5083410dacbff9c122c956ce2021-11-25T16:13:13ZFeature Selection for High-Dimensional Datasets through a Novel Artificial Bee Colony Framework10.3390/a141103241999-4893https://doaj.org/article/4684a31a5083410dacbff9c122c956ce2021-11-01T00:00:00Zhttps://www.mdpi.com/1999-4893/14/11/324https://doaj.org/toc/1999-4893There are generally many redundant and irrelevant features in high-dimensional datasets, which leads to the decline of classification performance and the extension of execution time. To tackle this problem, feature selection techniques are used to screen out redundant and irrelevant features. The artificial bee colony (ABC) algorithm is a popular meta-heuristic algorithm with high exploration and low exploitation capacities. To balance between both capacities of the ABC algorithm, a novel ABC framework is proposed in this paper. Specifically, the solutions are first updated by the process of employing bees to retain the original exploration ability, so that the algorithm can explore the solution space extensively. Then, the solutions are modified by the updating mechanism of an algorithm with strong exploitation ability in the onlooker bee phase. Finally, we remove the scout bee phase from the framework, which can not only reduce the exploration ability but also speed up the algorithm. In order to verify our idea, the operators of the grey wolf optimization (GWO) algorithm and whale optimization algorithm (WOA) are introduced into the framework to enhance the exploitation capability of onlooker bees, named BABCGWO and BABCWOA, respectively. It has been found that these two algorithms are superior to four state-of-the-art feature selection algorithms using 12 high-dimensional datasets, in terms of the classification error rate, size of feature subset and execution speed.Yuanzi ZhangJing WangXiaolin LiShiguo HuangXiuli WangMDPI AGarticleartificial bee colony algorithmhigh dimensionalityfeature selectionexploration–exploitation balanceIndustrial engineering. Management engineeringT55.4-60.8Electronic computers. Computer scienceQA75.5-76.95ENAlgorithms, Vol 14, Iss 324, p 324 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial bee colony algorithm high dimensionality feature selection exploration–exploitation balance Industrial engineering. Management engineering T55.4-60.8 Electronic computers. Computer science QA75.5-76.95
spellingShingle	artificial bee colony algorithm high dimensionality feature selection exploration–exploitation balance Industrial engineering. Management engineering T55.4-60.8 Electronic computers. Computer science QA75.5-76.95 Yuanzi Zhang Jing Wang Xiaolin Li Shiguo Huang Xiuli Wang Feature Selection for High-Dimensional Datasets through a Novel Artificial Bee Colony Framework
description	There are generally many redundant and irrelevant features in high-dimensional datasets, which leads to the decline of classification performance and the extension of execution time. To tackle this problem, feature selection techniques are used to screen out redundant and irrelevant features. The artificial bee colony (ABC) algorithm is a popular meta-heuristic algorithm with high exploration and low exploitation capacities. To balance between both capacities of the ABC algorithm, a novel ABC framework is proposed in this paper. Specifically, the solutions are first updated by the process of employing bees to retain the original exploration ability, so that the algorithm can explore the solution space extensively. Then, the solutions are modified by the updating mechanism of an algorithm with strong exploitation ability in the onlooker bee phase. Finally, we remove the scout bee phase from the framework, which can not only reduce the exploration ability but also speed up the algorithm. In order to verify our idea, the operators of the grey wolf optimization (GWO) algorithm and whale optimization algorithm (WOA) are introduced into the framework to enhance the exploitation capability of onlooker bees, named BABCGWO and BABCWOA, respectively. It has been found that these two algorithms are superior to four state-of-the-art feature selection algorithms using 12 high-dimensional datasets, in terms of the classification error rate, size of feature subset and execution speed.
format	article
author	Yuanzi Zhang Jing Wang Xiaolin Li Shiguo Huang Xiuli Wang
author_facet	Yuanzi Zhang Jing Wang Xiaolin Li Shiguo Huang Xiuli Wang
author_sort	Yuanzi Zhang
title	Feature Selection for High-Dimensional Datasets through a Novel Artificial Bee Colony Framework
title_short	Feature Selection for High-Dimensional Datasets through a Novel Artificial Bee Colony Framework
title_full	Feature Selection for High-Dimensional Datasets through a Novel Artificial Bee Colony Framework
title_fullStr	Feature Selection for High-Dimensional Datasets through a Novel Artificial Bee Colony Framework
title_full_unstemmed	Feature Selection for High-Dimensional Datasets through a Novel Artificial Bee Colony Framework
title_sort	feature selection for high-dimensional datasets through a novel artificial bee colony framework
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/4684a31a5083410dacbff9c122c956ce
work_keys_str_mv	AT yuanzizhang featureselectionforhighdimensionaldatasetsthroughanovelartificialbeecolonyframework AT jingwang featureselectionforhighdimensionaldatasetsthroughanovelartificialbeecolonyframework AT xiaolinli featureselectionforhighdimensionaldatasetsthroughanovelartificialbeecolonyframework AT shiguohuang featureselectionforhighdimensionaldatasetsthroughanovelartificialbeecolonyframework AT xiuliwang featureselectionforhighdimensionaldatasetsthroughanovelartificialbeecolonyframework
_version_	1718413253167546368

Feature Selection for High-Dimensional Datasets through a Novel Artificial Bee Colony Framework

Ejemplares similares