UM  > Faculty of Business Administration
Affiliated with RCfalse
Status已發表Published
Hydrological cycling optimization-based multiobjective feature-selection method for customer segmentation
Song, Xi1,2; Liu, Matthew Tingchi2; Liu, Qianying1; Niu, Ben1,3
2021-05-01
Source PublicationInternational Journal of Intelligent Systems
ISSN0884-8173
Volume36Issue:5Pages:2347-2366
Abstract

In the customer segmentation problem, a large number of features are manually designed and used to comprehensively describe the customer instances. However, some of these features are irrelevant, redundant, and noisy, which are not necessary and effective for customer segmentation. Feature selection is an important data preprocessing method by selecting important features from the original feature set. Particularly, feature selection in customer segmentation is a multiobjective problem that aims to minimize the feature number and maximize the classification performance. This paper proposes a multiobjective feature-selection method based on a meta-heuristic algorithm—hydrological cycling optimization (HCO)—to solve customer segmentation. The proposed method is able to automatically evolve a set of non-dominated solutions that select small numbers of features and achieve high classification accuracy. To this end, three strategies based on the global flow operator, possibility-based acceptance criteria, and density-based evaporation and precipitation are proposed to improve the global search ability and the solution diversity of the proposed approach. The performance of the proposed approach is examined on three customer-segmentation datasets and compared with original multiobjective HCO and six well-known evolutionary multiobjective algorithms. The results confirm the superiority of the proposed approach in solving multiobjective customer-segmentation problems by achieving higher calculation stability, search diversity, and solution quality compared with the other competing methods.

KeywordCustomer Segmentation Feature Selection Hydrological Cycling Optimization Multiobjective Optimization
DOI10.1002/int.22381
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000618016600001
Scopus ID2-s2.0-85101449845
Fulltext Access
Citation statistics
Cited Times [WOS]:7   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionFaculty of Business Administration
Corresponding AuthorNiu, Ben
Affiliation1.Department of Management Science and Engineering, College of Management, Shenzhen University, Shenzhen, China
2.Faculty of Business Administration, University of Macau, Macau Special Administrative Region, Taipa, China
3.Greater Bay Area International Institute for Innovation, Shenzhen University, Shenzhen, China
First Author AffilicationFaculty of Business Administration
Recommended Citation
GB/T 7714
Song, Xi,Liu, Matthew Tingchi,Liu, Qianying,et al. Hydrological cycling optimization-based multiobjective feature-selection method for customer segmentation[J]. International Journal of Intelligent Systems,2021,36(5):2347-2366.
APA Song, Xi,Liu, Matthew Tingchi,Liu, Qianying,&Niu, Ben.(2021).Hydrological cycling optimization-based multiobjective feature-selection method for customer segmentation.International Journal of Intelligent Systems,36(5),2347-2366.
MLA Song, Xi,et al."Hydrological cycling optimization-based multiobjective feature-selection method for customer segmentation".International Journal of Intelligent Systems 36.5(2021):2347-2366.
Files in This Item: Download All
File Name/Size Publications Version Access License
official copy-IJIS 2(1435KB)期刊论文作者接受稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Song, Xi]'s Articles
[Liu, Matthew Tingchi]'s Articles
[Liu, Qianying]'s Articles
Baidu academic
Similar articles in Baidu academic
[Song, Xi]'s Articles
[Liu, Matthew Tingchi]'s Articles
[Liu, Qianying]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Song, Xi]'s Articles
[Liu, Matthew Tingchi]'s Articles
[Liu, Qianying]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: official copy-IJIS 2021 April.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.