A comparison analysis for credit scoring using bagging ensembles. Issue 2 (11th June 2018)

• Record Type: Journal Article
• Authors: Luo, Cuicui
• Other Names: Wu Desheng Dash guestEditor.; Hall Jon guestEditor.; Belezamo Baloka guestEditor.; Eken Süleyman guestEditor.; Avci Cafer guestEditor.
• Is Part Of: Expert systems. Volume 39:Issue 2(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-06-11
• Subjects: artificial intelligence -- big data -- classifier ensembles -- risk assessment; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12297
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: In this paper, we present a hybrid approach for credit scoring, and the classification performance of this approach is compared with 4 base learners in machine learning. A large credit default swap dataset covering the period from 2006 to 2016 is used to build classifiers and test their performances. The results from this empirical study indicate that the bagging ensemble method can substantially improve individual base learners such as decision tree, multilayer perceptron, and k ‐nearest neighbours. The performance of support vector machine does not change after applying bagging ensemble. The overall results demonstrate that k ‐nearest neighbour is more suitable than any other method when dealing with large unbalanced datasets in credit scoring.