A relative evaluation of the performance of ensemble learning in credit scoring

Credit scoring prediction is a focus of banking sector to identify trickery customers and to reduce illegal activities. The usage of ensemble classifiers in machine learning plays a vital role in prediction problems. The aim of this study is to analyze the accuracy of the ensemble methods in classifying the customers as good risk group or bad risk group. In this paper experiments are conducted using three ensemble methods namely AdaBoost, Bagging, Random Forest combined with three learning algorithms. Feature selection is applied for selecting important attributes from credit card dataset. This paper provides an assessment on performance of the ensemble classifiers taken for this study.