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

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.