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Taylor and Francis Group and Juraj Dobrila University of Pula, Faculty of economics and tourism Dr. Mijo Mirković
Doi
Abstract
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
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