Prediction of credit delinquents using locally transductive multi-layer perceptron
SCIE
SCOPUS
- Title
- Prediction of credit delinquents using locally transductive multi-layer perceptron
- Authors
- Heo, H; Park, H; Kim, N; Lee, J
- Date Issued
- 2009-12
- Publisher
- ELSEVIER SCIENCE BV
- Abstract
- Many credit data classification problems require label predictions only for a given unlabeled test set. Since the number of an available unlabeled test data set is much larger than a labeled data set, it is desirable to build a predictive model in a transductive setting that takes advantage of the unlabeled data as well as labeled data. This paper proposes a localized transduction based multi-layer perceptron (MLP) methodology to build a better classifier. We provide a practical framework for our methodology. Simulations on real credit delinquents detection problems are conducted to test the proposed method with a promising result. © 2009 Elsevier B.V. All rights reserved.
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/31011
- DOI
- 10.1016/j.neucom.2009.02.025
- ISSN
- 0925-2312
- Article Type
- Article
- Citation
- NEUROCOMPUTING, vol. 73, no. 1-3, page. 169 - 175, 2009-12
- Files in This Item:
- There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.