Neural Networks for Prediction of Loan Default Using Attribute Relevance Analysis

被引：8

作者：

Reddy, M. V. Jagannatha ^{[1
]}

Kavitha, B. ^{[2
]}

机构：

[1] Madanapalle Inst Technol & Sci, Dept CSE, Madanapalle 517325, AP, India

[2] Sree Vidyaniketan Engn Coll, Dept MCA, Chittoor 51710, AP, India

来源：

2010 INTERNATIONAL CONFERENCE ON SIGNAL ACQUISITION AND PROCESSING: ICSAP 2010, PROCEEDINGS | 2010年

关键词：

Attribute relevance analysis; neural networks; prediction; defaulter;

D O I：

10.1109/ICSAP.2010.10

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Predicting the class label using neural networks through attribute relevance analysis is presented in this paper. This method has the advantage that the number of units required can be reduced so that we can increase the speed of neural network technique for predicting the class label of the new tuples. In this proposed paper attribute relevance analysis is used to eliminate irrelevant attributes to give as inputs to neural network. A simple neural network is used for testing class defaulter. The results shows that this method is feasible.

引用

页码：274 / 277

页数：4