Izvorni znanstveni članak
https://doi.org/10.1080/1331677X.2020.1867213
Feature selection in credit risk modeling: an international evidence
Ying Zhou
Mohammad Shamsu Uddin
Tabassum Habib
Guotai Chi
Kunpeng Yuan
Sažetak
This paper aims to discover a suitable combination of contemporary feature selection techniques and robust prediction classifiers.
As such, to examine the impact of the feature selection method
on classifier performance, we use two Chinese and three other
real-world credit scoring datasets. The utilized feature selection
methods are the least absolute shrinkage and selection operator
(LASSO), multivariate adaptive regression splines (MARS). In contrast, the examined classifiers are the classification and regression
trees (CART), logistic regression (LR), artificial neural network
(ANN), and support vector machines (SVM). Empirical findings
confirm that LASSO’s feature selection method, followed by
robust classifier SVM, demonstrates remarkable improvement and
outperforms other competitive classifiers. Moreover, ANN also
offers improved accuracy with feature selection methods; LR only
can improve classification efficiency through performing feature
selection via LASSO. Nonetheless, CART does not provide any
indication of improvement in any combination. The proposed
credit scoring modeling strategy may use to develop policy, progressive ideas, operational guidelines for effective credit risk management of lending, and other financial institutions. The finding
of this study has practical value, as to date, there is no consensus
about the combination of feature selection method and prediction classifiers.
Ključne riječi
Credit risk; feature selection; least absolute shrinkage and selection operator; support vector machines
Hrčak ID:
301473
URI
Datum izdavanja:
31.12.2021.
Posjeta: 275 *