A Hybrid Model Using Decision Tree and Neural Network for Credit Scoring Problem

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Provided by: Growing Science
Topic: Data Management
Format: PDF
Now-a-days, 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. 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 assembling them with voting methods.
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