C4.5 Decision Tree Enhanced with AdaBoost Versus Multilayer Perceptron for Credit Scoring Modeling

Abstract

Within this paper, we evaluate two main machine learning techniques for credit scoring. The first algorithm consists of a cascade with two steps: (a) C4.5 decision tree; (b) AdaBoost for binary classification (credit accepted or rejected). The second technique corresponds to choosing a neural network classifier implemented by Multilayer Perceptron (MLP). For evaluation of the proposed models, we have used the German credit dataset and the Australian credit dataset. For the German dataset, MLP leads to the best result corresponding to an accuracy of 81.0%, versus C4.5 enhanced with AdaBoost that leads to an accuracy of 78.67%. For the Australian credit dataset, we found that MLP is also the best classifier with an accuracy of 90.85%, versus C4.5 followed by AdaBoost obtaining an accuracy of 89.00%. At the same time, one can remark that C4.5 enhanced by AdaBoost has led to a better performance than a simple C4.5 .