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Example-dependent cost-sensitive logistic regression for credit scoring
(Signal Processing)ORCID iD: 0000-0003-2298-6774
2014 (English)In: 2014 13th International Conference on Machine Learning and Applications, IEEE conference proceedings, 2014, 263-269 p.Conference paper, (Refereed)
Abstract [en]

Several real-world classification problems are example-dependent cost-sensitive in nature, where the costs due to misclassification vary between examples. Credit scoring is a typical example of cost-sensitive classification. However, it is usually treated using methods that do not take into account the real financial costs associated with the lending business. In this paper, we propose a new example-dependent cost matrix for credit scoring. Furthermore, we propose an algorithm that introduces the example-dependent costs into a logistic regression. Using two publicly available datasets, we compare our proposed method against state-of-the-art example-dependent cost-sensitive algorithms. The results highlight the importance of using real financial costs. Moreover, by using the proposed cost-sensitive logistic regression, significant improvements are made in the sense of higher savings.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2014. 263-269 p.
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-163750DOI: 10.1109/ICMLA.2014.48Scopus ID: 2-s2.0-84924954838OAI: oai:DiVA.org:kth-163750DiVA: diva2:1082109
Conference
ICMLA
Note

QC 20170322

Available from: 2017-03-15 Created: 2017-03-15 Last updated: 2017-03-22Bibliographically approved

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Ottersten, Björn
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