A new hybrid ensemble credit scoring model based on classifiers consensus system approach
A new hybrid ensemble model for credit scoring problem is proposed.An improved data filtering technique is developed based on GNG method.GNG with MARS combined proved to be better than applying them individually.Our model is validated on four performance measures over seven credit datasets.Classifiers decisions after consensus effectively improved prediction performance. During the last few years there has been marked attention towards hybrid and ensemble systems development, having proved their ability to be more accurate than single classifier models. However, among the hybrid and ensemble models developed in the literature there has been little consideration given to: 1) combining data filtering and feature selection methods 2) combining classifiers of different algorithms; and 3) exploring different classifier output combination techniques other than the traditional ones found in the literature. In this paper, the aim is to improve predictive performance by presenting a new hybrid ensemble credit scoring model through the combination of two data pre processing methods based on Gabriel Neighbourhood Graph editing (GNG) and Multivariate Adaptive Regression Splines (MARS) in the hybrid modelling phase. In addition, a new classifier combination rule based on the consensus approach (ConsA) of different classification algorithms during the ensemble modelling phase is proposed. Several comparisons will be carried out in this paper, as follows: 1) Comparison of individual base classifiers with the GNG and MARS methods applied separately and combined in order to choose the best results for the ensemble modelling phase; 2) Comparison of the proposed approach with all the base classifiers and ensemble classifiers with the traditional combination methods; and 3) Comparison of the proposed approach with recent related studies in the literature. Five of the well known base classifiers are used, namely, neural networks (NN) support vector machines (SVM) random forests (RF) decision trees (DT) and naive Bayes (NB) The experimental results, analysis and statistical tests prove the ability of the proposed approach to improve prediction performance against all the base classifiers, hybrid and the traditional combination methods in terms of average accuracy, the area under the curve (AUC) H measure and the Brier Score. The model was validated over seven real world credit datasets.
Expert Syst. Appl.