Abstract
AbstractIn 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.
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