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.

摘要

本文旨在发现当代特征选择技术和鲁棒预测分类器的合适组合。因此，为了检查特征选择方法对分类器性能的影响，我们使用了两个中文和三个其他真实世界的信用评分数据集。使用的特征选择方法是最小绝对收缩和选择算子（LASSO），多元自适应回归样条（MARS）。相比之下，检查的分类器是分类和回归树 (CART)、逻辑回归 (LR)、人工神经网络 (ANN) 和支持向量机 (SVM)。实证结果证实，LASSO 的特征选择方法以及鲁棒的分类器 SVM 显示出显着的改进并优于其他竞争分类器。而且，ANN 还通过特征选择方法提高了准确性；LR 只能通过 LASSO 进行特征选择来提高分类效率。尽管如此，CART 并未提供任何组合改进的任何迹象。建议的信用评分建模策略可用于制定政策、进步理念、操作指南，以对贷款和其他金融机构进行有效的信用风险管理。本研究的发现具有实用价值，迄今为止，关于特征选择方法与预测分类器的结合尚无共识。建议的信用评分建模策略可用于制定政策、进步理念、操作指南，以对贷款和其他金融机构进行有效的信用风险管理。本研究的发现具有实用价值，迄今为止，关于特征选择方法与预测分类器的结合尚无共识。建议的信用评分建模策略可用于制定政策、进步理念、操作指南，以对贷款和其他金融机构进行有效的信用风险管理。本研究的发现具有实用价值，迄今为止，关于特征选择方法与预测分类器的结合尚无共识。