COMBINING CLASSIFIERS FOR CREDIT RISK PREDICTION

作     者：Bhekisipho TWALA 

作者机构：CSIR Modelling and Digital Sciences Unit P.O. Box 395 Pretoria 0001South Africa 

出 版 物：《Journal of Systems Science and Systems Engineering》 (系统科学与系统工程学报（英文版）)

年 卷 期：2009年第18卷第3期

页      面：292-311页

核心收录：

学科分类：0810[工学-信息与通信工程] 1205[管理学-图书情报与档案管理] 12[管理学] 120202[管理学-企业管理（含：财务管理、市场营销、人力资源管理）] 1202[管理学-工商管理] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：funded by the CSIR under project MDSARR1 

主　　题：Supervised learning statistical pattern recognition ensemble credit risk prediction 

摘      要：Credit risk prediction models seek to predict quality factors such as whether an individual will default （bad applicant） on a loan or not （good applicant）. This can be treated as a kind of machine learning （ML） problem. Recently, the use of ML algorithms has proven to be of great practical value in solving a variety of risk problems including credit risk prediction. One of the most active areas of recent research in ML has been the use of ensemble （combining） classifiers. Research indicates that ensemble individual classifiers lead to a significant improvement in classification performance by having them vote for the most popular class. This paper explores the predicted behaviour of five classifiers for different types of noise in terms of credit risk prediction accuracy, and how could such accuracy be improved by using pairs of classifier ensembles. Benchmarking results on five credit datasets and comparison with the performance of each individual classifier on predictive accuracy at various attribute noise levels are presented. The experimental evaluation shows that the ensemble of classifiers technique has the potential to improve prediction accuracy.