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Open AccessArticle

On Ensemble SSL Algorithms for Credit Scoring Problem

Computer & Informatics Engineering Department, Technological Educational Institute of Western Greece, GR 263-34 Antirion, Greece
Department of Statistics and Insurance Science, University of Piraeus, GR 185-34 Piraeus, Greece
Department of Mathematics, University of Patras, Patras, GR 265-00, Greece
Author to whom correspondence should be addressed.
Informatics 2018, 5(4), 40;
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.

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