NDLI: Mining the customer credit by using the neural network model with classification and regression tree approach

Content Provider	IEEE Xplore Digital Library
Author	Ling Jing Kao Chih Chou Chiu
Copyright Year	2001
Description	Author affiliation: Dept. of Stat., Texas A&M Univ., College Station, TX, USA (Ling Jing Kao)
Abstract	A combination of classification and regression trees (CART) and neural network techniques is proposed to determine whether the predictive capability can be enhanced in a credit-scoring model. To demonstrate the effectiveness of the proposed approach, these techniques are applied to data from a large bank in Taiwan. In the neural network and the combined model approaches, the backpropagation learning technique, with various learning rates, was extensively studied to determine the connection weights between the neurons. The number of hidden neurons was also varied to determine its effect on the convergence rate. Our results indicate that the proposed combined approach predicts much more accurately and converges much faster than that the conventional CART method or the neural network approach.
Sponsorship	Int. Fuzzy Syst. Assoc.
Starting Page	923
Ending Page	928
File Size	519588
Page Count	6
File Format	PDF
ISBN	0780370783
DOI	10.1109/NAFIPS.2001.944728
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-07-25
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Classification tree analysis Regression tree analysis Logistics Linear discriminant analysis Statistics Predictive models Neurons Statistical analysis Artificial neural networks
Content Type	Text
Resource Type	Article

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