Generalized fuzzy soft sets theory‐based novel hybrid ensemble credit scoring model

Abstract

In banking and peer‐to‐peer loan applicant firms, customer credit scores have numerous applications in risk control and precision marketing. Numerous credit scoring techniques act as classification methods. In this paper, the main issue is simultaneous and hybrid utilization of the feature selection (FS) algorithm and ensemble learning classification algorithms with respect to their parameter settings to achieve higher performance in the proposed credit scoring model. As a result, this paper reports a hybrid data mining model of generalized fuzzy soft sets (GFSS) theory‐based ensemble learning classification algorithms based on three stages. The first stage addresses data gathering and preprocessing. The second stage uses the adaptive elastic net‐based FS algorithm to eliminate irrelevant or weakly correlated variables. After the appropriate variables are chosen, they are applied to the proposed ensemble model. In the third stage, GFSS theory is exploited to construct a novel weight assignment mechanism for each individual credit scoring model in the ensemble model according to performance. Several comparisons are conducted to validate the proposed model using real world credit datasets. The experimental results, analysis, and statistical tests prove the ability of the proposed approach to improve classification performance against all of the base classifier, hybrid, and combination methods in terms of average accuracy, area under the curve, H‐measure, and Brier score.

Optimizing hyper-parameters of neural networks with swarm intelligence: A novel framework for credit scoring

“…This paper carries out an experiment to test whether the BP-ANN model trained by swarm intelligence algorithm (treated group) outperforms prevalent classical models (control group) and several typical hybrid or ensemble models constructed in recent literature [29][30][31][32][33][34] within the context of credit scoring. First, the experiment investigates the performance of seven swarm intelligence algorithms (see Section "Swarm intelligence algorithm") as an optimizer of BP-ANN model in the context of different datasets.…”

“…logistic regression, NB approach, DA, KNN, DT, SVM, K means, and RF) are enrolled in control group. Within the context of the same public datasets, we enroll several typical hybrid or ensemble models proposed in recent literature [29][30][31][32][33][34] in the control group. When evaluating the performance of difference models, we report the value of eight indicators in Section "Model evaluation" while focusing on the value of AUC (see Section "Model evaluation" for detailed calculation).…”

“…Thus, the purpose of this research is to construct a realistic framework tailored for credit scoring to optimize the hyper-parameters of neural network with swarm intelligence algorithm. This paper further benchmarks the performance of the novel framework against classical as well as hybrid or ensemble models proposed in recent literature [29][30][31][32][33][34]. This paper is to answer the following questions.…”

“…First, does the neural network with hyper-parameters determined by swarm intelligence algorithm outperform the classical credit-scoring models (i.e. logistic regression, naive Bayesian, discriminant analysis, k nearest neighbor, decision tree, support-vector machine, K-means, and random forest) and state-of-the-art models proposed in recent literature [29][30][31][32][33][34]? Second, are the fitting and generalization ability of a neural network steady after its parameters determined by swarm intelligence algorithms?…”

“…To address this purpose, this paper proposes a novel credit-scoring framework to determine the optimal SI algorithm for hyper-parameter optimization of neural network and carries out an experiment to test the generalization and robustness of neural network trained by swarm intelligence algorithm. Specifically, eight other prevalent credit-scoring models as well as some hybrid or ensemble models constructed in recent literature [29][30][31][32][33][34] make up the control group and seven swarm intelligence algorithms are extracted from prior literature. The neural network with parameters trained by the seven swarm intelligence algorithms are included in the treated group.…”