Embedded variable selection method using signomial classification

Kyoungmi Hwang (), Dohyun Kim (), Kyungsik Lee (), Chungmok Lee () and Sungsoo Park ()
Additional contact information
Kyoungmi Hwang: Samsung Electronics
Dohyun Kim: Myongji University
Kyungsik Lee: Seoul National University
Chungmok Lee: Hankuk University of Foreign Studies
Sungsoo Park: KAIST

Annals of Operations Research, 2017, vol. 254, issue 1, No 6, 89-109

Abstract: Abstract We propose two variable selection methods using signomial classification. We attempt to select, among a set of the input variables, the variables that lead to the best performance of the classifier. One method repeatedly removes variables based on backward selection, whereas the second method directly selects a set of variables by solving an optimization problem. The proposed methods conduct variable selection considering nonlinear interactions of variables and obtain a signomial classifier with the selected variables. Computational results show that the proposed methods more effectively selects desirable variables for predicting output and provide the classifiers with better or comparable test error rates, as compared with existing methods.

Keywords: Classification problems; Variable selection; Embedded method; Signomial classification (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://link.springer.com/10.1007/s10479-017-2445-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:annopr:v:254:y:2017:i:1:d:10.1007_s10479-017-2445-z

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10479

DOI: 10.1007/s10479-017-2445-z

Access Statistics for this article

Annals of Operations Research is currently edited by Endre Boros

More articles in Annals of Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().