Openly accessible

DGHNL: A new deep genetic hierarchical network of learners for prediction of credit scoring

Pławiak, Pawel, Abdar, Moloud, Pławiak, Joanna, Makarenkov, Vladimir and Acharya, U. Rajendra 2020, DGHNL: A new deep genetic hierarchical network of learners for prediction of credit scoring, Information Sciences, vol. 516, pp. 401-418, doi: 10.1016/j.ins.2019.12.045.

Attached Files
Name Description MIMEType Size Downloads

Title DGHNL: A new deep genetic hierarchical network of learners for prediction of credit scoring
Author(s) Pławiak, Pawel
Abdar, MoloudORCID iD for Abdar, Moloud orcid.org/0000-0002-3059-6357
Pławiak, Joanna
Makarenkov, Vladimir
Acharya, U. Rajendra
Journal name Information Sciences
Volume number 516
Start page 401
End page 418
Total pages 18
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2020-04
ISSN 0020-0255
Keyword(s) Credit scoring
Machine learning
Data mining
Ensemble learning
Deep learning
Genetic algorithm
Feature extraction and selection
Language eng
DOI 10.1016/j.ins.2019.12.045
Indigenous content off
Field of Research 01 Mathematical Sciences
08 Information and Computing Sciences
09 Engineering
HERDC Research category C1 Refereed article in a scholarly journal
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30134281

Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 8 times in TR Web of Science
Scopus Citation Count Cited 10 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 17 Abstract Views, 0 File Downloads  -  Detailed Statistics
Created: Fri, 31 Jan 2020, 13:41:34 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.