Estimation of optimum thresholds for binary classification using genetic algorithm: An application to solve a credit scoring problem

Abstract

AbstractThe main issue in a classification problem is classifying observations into various disjoint classes. Different classification techniques generate a continuous number between a and b, usually between 0 and 1; thus, the optimal cut‐off value(s) must be carefully selected to discriminate classes precisely. The decision is about setting a threshold value and transforming the continuous score into a binary output. Therefore, in addition to using the so‐called sophisticated classification methods to have a more accurate classification, there is a need to identify and choose the optimal threshold value(s). However, the latter has not been thoroughly investigated. Hence, this study proposes an approach based on a Genetic Algorithm (GA) and Neural Networks (NNs) to automatically find customized cut‐off values, considering different performance criteria and given datasets. Since credit scoring is a binary classification problem, two popular credit scoring datasets, namely “Australian” and “German” credit datasets, are used to test the proposed approach. Our numerical results revealed that the proposed GA‐NN model could successfully find customized acceptance thresholds, considering predetermined performance criteria, including Accuracy, Estimated Misclassification Cost (EMC), and Area under ROC Curve (AUC) for the tested datasets. Furthermore, the best‐obtained results and the paired‐samples t‐test results show that utilizing the customized cut‐off points leads to a more accurate classification than the commonly‐used threshold value of 0.5.