Next Article in Journal
Theory and Practice in Digital Behaviour Change: A Matrix Framework for the Co-Production of Digital Services That Engage, Empower and Emancipate Marginalised People Living with Complex and Chronic Conditions
Previous Article in Journal
Large Scale Advanced Data Analytics on Skin Conditions from Genotype to Phenotype
Open AccessArticle

On Ensemble SSL Algorithms for Credit Scoring Problem

1
Computer & Informatics Engineering Department, Technological Educational Institute of Western Greece, GR 263-34 Antirion, Greece
2
Department of Statistics and Insurance Science, University of Piraeus, GR 185-34 Piraeus, Greece
3
Department of Mathematics, University of Patras, Patras, GR 265-00, Greece
*
Author to whom correspondence should be addressed.
Informatics 2018, 5(4), 40; https://doi.org/10.3390/informatics5040040
Received: 17 September 2018 / Revised: 23 October 2018 / Accepted: 26 October 2018 / Published: 28 October 2018
Credit scoring is generally recognized as one of the most significant operational research techniques used in banking and finance, aiming to identify whether a credit consumer belongs to either a legitimate or a suspicious customer group. With the vigorous development of the Internet and the widespread adoption of electronic records, banks and financial institutions have accumulated large repositories of labeled and mostly unlabeled data. Semi-supervised learning constitutes an appropriate machine- learning methodology for extracting useful knowledge from both labeled and unlabeled data. In this work, we evaluate the performance of two ensemble semi-supervised learning algorithms for the credit scoring problem. Our numerical experiments indicate that the proposed algorithms outperform their component semi-supervised learning algorithms, illustrating that reliable and robust prediction models could be developed by the adaptation of ensemble techniques in the semi-supervised learning framework. View Full-Text
Keywords: semi-supervised learning; self-labeled methods; ensemble learning; credit scoring; classification semi-supervised learning; self-labeled methods; ensemble learning; credit scoring; classification
MDPI and ACS Style

Livieris, I.E.; Kiriakidou, N.; Kanavos, A.; Tampakas, V.; Pintelas, P. On Ensemble SSL Algorithms for Credit Scoring Problem. Informatics 2018, 5, 40.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop