On Ensemble SSL Algorithms for Credit Scoring Problem
AbstractCredit 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
Share & Cite This Article
Livieris, I.E.; Kiriakidou, N.; Kanavos, A.; Tampakas, V.; Pintelas, P. On Ensemble SSL Algorithms for Credit Scoring Problem. Informatics 2018, 5, 40.
Livieris IE, Kiriakidou N, Kanavos A, Tampakas V, Pintelas P. On Ensemble SSL Algorithms for Credit Scoring Problem. Informatics. 2018; 5(4):40.Chicago/Turabian Style
Livieris, Ioannis E.; Kiriakidou, Niki; Kanavos, Andreas; Tampakas, Vassilis; Pintelas, Panagiotis. 2018. "On Ensemble SSL Algorithms for Credit Scoring Problem." Informatics 5, no. 4: 40.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.