A Binary Superior Tracking Artificial Bee Colony with Dynamic Cauchy Mutation for Feature Selection

A Binary Superior Tracking Artificial Bee Colony with Dynamic Cauchy Mutation for Feature Selection

This paper aims to propose an improved learning algorithm for feature selection, termed as binary superior tracking artificial bee colony with dynamic Cauchy mutation (BSTABC-DCM). To enhance exploitation capacity, a binary learning strategy is proposed to enable each bee to learn from the superior...

Full description

Bibliographic Details
Main Authors: Xianghua Chu, Shuxiang Li, Da Gao, Wei Zhao, Jianshuang Cui, Linya Huang
Format: Article
Language:English
Published: Hindawi-Wiley 2020-01-01
Series:Complexity
Online Access:http://dx.doi.org/10.1155/2020/8864315
id doaj-073de47e0cf94570b8c3a70530fa12c7
record_format Article
spelling doaj-073de47e0cf94570b8c3a70530fa12c72020-11-25T04:11:59ZengHindawi-WileyComplexity1076-27871099-05262020-01-01202010.1155/2020/88643158864315A Binary Superior Tracking Artificial Bee Colony with Dynamic Cauchy Mutation for Feature SelectionXianghua Chu0Shuxiang Li1Da Gao2Wei Zhao3Jianshuang Cui4Linya Huang5College of Management, Shenzhen University, Shenzhen, ChinaCollege of Management, Shenzhen University, Shenzhen, ChinaCollege of Management, Shenzhen University, Shenzhen, ChinaCollege of Management, Shenzhen University, Shenzhen, ChinaSchool of Economics and Management, University of Science and Technology Beijing, Beijing, ChinaCollege of Management, Shenzhen University, Shenzhen, ChinaThis paper aims to propose an improved learning algorithm for feature selection, termed as binary superior tracking artificial bee colony with dynamic Cauchy mutation (BSTABC-DCM). To enhance exploitation capacity, a binary learning strategy is proposed to enable each bee to learn from the superior individuals in each dimension. A dynamic Cauchy mutation is introduced to diversify the population distribution. Ten datasets from UCI repository are adopted as test problems, and the average results of cross-validation of BSTABC-DCM are compared with other seven popular swarm intelligence metaheuristics. Experimental results demonstrate that BSTABC-DCM could obtain the optimal classification accuracy and select the best representative features for the UCI problems.http://dx.doi.org/10.1155/2020/8864315
collection DOAJ
language English
format Article
sources DOAJ
author Xianghua Chu
Shuxiang Li
Da Gao
Wei Zhao
Jianshuang Cui
Linya Huang
spellingShingle Xianghua Chu
Shuxiang Li
Da Gao
Wei Zhao
Jianshuang Cui
Linya Huang
A Binary Superior Tracking Artificial Bee Colony with Dynamic Cauchy Mutation for Feature Selection
Complexity
author_facet Xianghua Chu
Shuxiang Li
Da Gao
Wei Zhao
Jianshuang Cui
Linya Huang
author_sort Xianghua Chu
title A Binary Superior Tracking Artificial Bee Colony with Dynamic Cauchy Mutation for Feature Selection
title_short A Binary Superior Tracking Artificial Bee Colony with Dynamic Cauchy Mutation for Feature Selection
title_full A Binary Superior Tracking Artificial Bee Colony with Dynamic Cauchy Mutation for Feature Selection
title_fullStr A Binary Superior Tracking Artificial Bee Colony with Dynamic Cauchy Mutation for Feature Selection
title_full_unstemmed A Binary Superior Tracking Artificial Bee Colony with Dynamic Cauchy Mutation for Feature Selection
title_sort binary superior tracking artificial bee colony with dynamic cauchy mutation for feature selection
publisher Hindawi-Wiley
series Complexity
issn 1076-2787
1099-0526
publishDate 2020-01-01
description This paper aims to propose an improved learning algorithm for feature selection, termed as binary superior tracking artificial bee colony with dynamic Cauchy mutation (BSTABC-DCM). To enhance exploitation capacity, a binary learning strategy is proposed to enable each bee to learn from the superior individuals in each dimension. A dynamic Cauchy mutation is introduced to diversify the population distribution. Ten datasets from UCI repository are adopted as test problems, and the average results of cross-validation of BSTABC-DCM are compared with other seven popular swarm intelligence metaheuristics. Experimental results demonstrate that BSTABC-DCM could obtain the optimal classification accuracy and select the best representative features for the UCI problems.
url http://dx.doi.org/10.1155/2020/8864315
work_keys_str_mv AT xianghuachu abinarysuperiortrackingartificialbeecolonywithdynamiccauchymutationforfeatureselection
AT shuxiangli abinarysuperiortrackingartificialbeecolonywithdynamiccauchymutationforfeatureselection
AT dagao abinarysuperiortrackingartificialbeecolonywithdynamiccauchymutationforfeatureselection
AT weizhao abinarysuperiortrackingartificialbeecolonywithdynamiccauchymutationforfeatureselection
AT jianshuangcui abinarysuperiortrackingartificialbeecolonywithdynamiccauchymutationforfeatureselection
AT linyahuang abinarysuperiortrackingartificialbeecolonywithdynamiccauchymutationforfeatureselection
AT xianghuachu binarysuperiortrackingartificialbeecolonywithdynamiccauchymutationforfeatureselection
AT shuxiangli binarysuperiortrackingartificialbeecolonywithdynamiccauchymutationforfeatureselection
AT dagao binarysuperiortrackingartificialbeecolonywithdynamiccauchymutationforfeatureselection
AT weizhao binarysuperiortrackingartificialbeecolonywithdynamiccauchymutationforfeatureselection
AT jianshuangcui binarysuperiortrackingartificialbeecolonywithdynamiccauchymutationforfeatureselection
AT linyahuang binarysuperiortrackingartificialbeecolonywithdynamiccauchymutationforfeatureselection
_version_ 1715033068424658944

Similar Items