DocumentCode
3342855
Title
Notice of Retraction
Credit scoring model based on selective neural network ensemble
Author
Xiang Hui ; Yang Sheng Gang
Author_Institution
Coll. of Econ. & Manage., Hunan Normal Univ., Changsha, China
Volume
1
fYear
2011
fDate
26-28 July 2011
Firstpage
513
Lastpage
516
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Credit scoring has gained increasing attentions from banks, which can benefit from reducing possible risks of default. Based on the analysis of relationship between the performance of ensemble model and that of base classifiers, this paper proposes a selective neural network ensemble model for credit scoring, In which Artificial neural networks and ensemble learning methods are firstly employed to build a base classifiers pool, then hierarchical clustering algorithm is used to divide those base classifiers into several clusters, then the classifiers with highest accuracy in each cluster are chose to vote for the final decision. Three real world credit datasets are selected as the experimental data to demonstrate the accuracy of the model. The results show that selective neural network ensemble model can significantly improved the efficiency in selection of base classifiers and generalization ability and thereby show enough attractive features for credit risk management system.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Credit scoring has gained increasing attentions from banks, which can benefit from reducing possible risks of default. Based on the analysis of relationship between the performance of ensemble model and that of base classifiers, this paper proposes a selective neural network ensemble model for credit scoring, In which Artificial neural networks and ensemble learning methods are firstly employed to build a base classifiers pool, then hierarchical clustering algorithm is used to divide those base classifiers into several clusters, then the classifiers with highest accuracy in each cluster are chose to vote for the final decision. Three real world credit datasets are selected as the experimental data to demonstrate the accuracy of the model. The results show that selective neural network ensemble model can significantly improved the efficiency in selection of base classifiers and generalization ability and thereby show enough attractive features for credit risk management system.
Keywords
finance; learning (artificial intelligence); neural nets; pattern classification; risk management; artificial neural networks; banks; base classifiers; credit risk management system; credit scoring model; ensemble learning methods; generalization ability; hierarchical clustering algorithm; selective neural network ensemble model; Accuracy; Bagging; Boosting; Classification algorithms; Clustering algorithms; Data models; Credit scoring; clustering; selective ensemble;
fLanguage
English
Publisher
ieee
Conference_Titel
Natural Computation (ICNC), 2011 Seventh International Conference on
Conference_Location
Shanghai
ISSN
2157-9555
Print_ISBN
978-1-4244-9950-2
Type
conf
DOI
10.1109/ICNC.2011.6022104
Filename
6022104
Link To Document