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Credit Scoring Using Soft Computing Schemes: A Comparison between Support Vector Machines and Artificial Neural Networks. (Nwulu, Nnamdi I.)
Bibliographical information (record 268548)
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Credit Scoring Using Soft Computing Schemes: A Comparison between Support Vector Machines and Artificial Neural Networks.
Author:
 Nwulu, Nnamdi I. Search Author in Amazon Books

Publisher:
Springer,
Edition:
 2011.
Classification:
 QA76.9
Detailed notes
    - The recent financial crisis that has devastated many nations of the world has made it imperative that nations upgrade their credit scoring methods. Although statistical methods have been the preferred method for decades, soft computing techniques are becoming increasingly popular due to their efficient and accurate nature and relative simplicity. In this paper a comparison is made between two prominent soft computing schemes namely Support Vector Machines and Artificial Neural Networks. Although a comparison can be made along various criteria, this study attempts to compare both techniques when applied to credit scoring in terms of accuracy, computational complexity and processing times. In order to assure meaningful comparisons, a real world dataset precisely the Australian Credit Scoring data set available online was used for this task. Experimental results obtained indicate that although both soft computing schemes are highly efficient, Artificial Neural Networks obtain slightly better results and in relatively shorter times.
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Status
Library
Section
EOL-1642
Item available
NEU Grand LibraryOnline (QA76.9 .C74 2011)
Online electronic
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