The Use of Genetic Algorithm, Clustering and Feature Selection Techniques in Construction of Decision Tree Models for Credit Scoring

Abstract

Decision tree modelling, as one of data mining techniques, is used for credit scoring of bank customers. The main problem is the construction of decision trees that could classify customers optimally. This study presents a new hybrid mining approach in the design of an effective and appropriate credit scoring model. It is based on genetic algorithm for credit scoring of bank customers in order to offer credit facilities to each class of customers. Genetic algorithm can help banks in credit scoring of customers by selecting appropriate features and building optimum decision trees. The new proposed hybrid classification model is established based on a combination of clustering, feature selection, decision trees, and genetic algorithm techniques. We used clustering and feature selection techniques to pre-process the input samples to construct the decision trees in the credit scoring model. The proposed hybrid model choices and combines the best decision trees based on the optimality criteria. It constructs the final decision tree for credit scoring of customers. Using one credit data set, results confirm that the classification accuracy of the proposed hybrid classification model is more than almost the entire classification models that have been compared in this paper. Furthermore, the number of leaves and the size of the constructed decision tree (i.e. complexity) are less, compared with other decision tree models. In this work, one financial data set was chosen for experiments, including Bank Mellat credit data set.