文章基本信息

标题：A hybrid model using decision tree and neural network for credit scoring problem ,
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作者：Amir Arzy Soltan ; Mohammad Mehrabioun Mohammadi
期刊名称：Management Science Letters
印刷版ISSN：1923-9335
电子版ISSN：1923-9343
出版年度：2012
卷号：2
期号：5
页码：1683-1688
DOI：10.5267/j.msl.2012.04.021
出版社：Growing Science
摘要：Nowadays credit scoring is an important issue for financial and monetary organizations that has substantial impact on reduction of customer attraction risks. Identification of high risk customer can reduce finished cost. An accurate classification of customer and low type 1 and type 2 errors have been investigated in many studies. The primary objective of this paper is to develop a new method, which chooses the best neural network architecture based on one column hidden layer MLP, multiple columns hidden layers MLP, RBFN and decision trees and ensembling them with voting methods. The proposed method of this paper is run on an Australian credit data and a private bank in Iran called Export Development Bank of Iran and the results are used for making solution in low customer attraction risks
关键词：BPNN; Neural network; Data mining; Information Technology