An uncertainty-oriented cost-sensitive credit scoring framework with multi-objective feature selection

Abstract

• An uncertainty-oriented credit scoring framework with multi-objective feature selection is developed to tackle the credit classification task under uncertainty. • This study extends the use of cost space to feature selection in the credit scoring process. • The proposed credit scoring framework can provide a series of Pareto-optimal credit scoring models to fit different decision-making contexts. • The cost plot enables credit decision-makers to see the results of different credit scoring models with different risk sources and corresponding expected costs. In order to solve the problem of uncertain misclassification costs and class distributions in credit scoring tasks, an uncertainty-oriented credit scoring framework based on a multi-objective feature selection strategy is proposed in this study. This proposed framework searches for a pool of Pareto-optimal credit scoring models with different feature subsets without the assumption of the operating condition (misclassification costs and class distributions) information. Specifically, the searching process concerns the trade-off of the False Positive Rate and the False Negative Rate using a binary multi-objective particle swarm optimization (BMOPSO) algorithm. By visualizing the Pareto-optimal solutions in cost space, credit decision-makers can select an optimal compromise model based on their decision-making contexts. The proposed framework is compared with baseline models on three retail credit scoring datasets. The experimental results show that the proposed framework could find out the optimal credit model with minimal misclassification cost for almost all possible operating conditions.