Entropy multi-hyperplane credit scoring model

Entropy multi-hyperplane credit scoring model is a decision model that classifies applicants into payers or defaulters by optimizing the classification cost using multiple hyperplanes based on entropy order. In the first stage, the model uses a pair of hyperplanes composed of half of the attributes which are ordered increasingly by the entropy. The hyperplanes divide the applicants into 3 groups, namely payers, defaulters and the unclassified. From the unclassified group, the model uses another pair of hyperplanes which are composed of additional half of the rest of attributes based on the previous entropy order. The additional hyperplanes divide the unclassified group in the first stage into another 3 group namely payers in 2^nd stage, defaulters in 2^nd stage and the unclassified in 2^nd stage. In the final stage, the multidimensional hyperplanes created from all attributes are used to divide the loaners into two groups : payers and defaulters. In this paper, a mixed-integer programming model for entropy multi-hyperplane credit scoring model is developed to minimize the cost of misclassification errors. The experiment shows that our model has more accuracy than a two-stage least cost credit scoring model and uses less computational iterations than a multi-hyperplane credit scoring model. Moreover, the new model exhibits comparable result with classification tree, neural network, support vector machine, linear discriminant analysis and CART.