doi: 10.7763/IJCTE.2010.V2.208
Extracting Prediction Rules for Loan Default Using Neural Networks through Attribute Relevance Analysis
Abstract
Predicting the class label loan defaulter using neural networks through attribute relevance analysis is presented in the previous paper. In this paper we are extracting prediction rules from the predicted class label. This method has the advantage that the number of units required can be reduced using attribute relevance analysis. 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. Neural networks used for predicting the class label and deriving the prediction rules from the class label. These rules are more useful in understanding the customer.
Keywords
- Classification rules
- Attribute relevance analysis
- neural networks
- prediction rules
- defaulter
- classlabel
How to Cite
M. V. Jagannatha Reddy and B. Kavitha, "Extracting Prediction Rules for Loan Default Using Neural Networks through Attribute Relevance Analysis," International Journal of Computer Theory and Engineering, vol. 2, no. 4, pp. 596-601, 2010. https://doi.org/10.7763/IJCTE.2010.V2.208
Copyright & License
Copyright © 2010 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).