A bank customer credit evaluation based on the decision tree and the simulated annealing algorithm

Jiang, Y; Chen, Y; Zeng, Z; He, X

A bank customer credit evaluation based on the decision tree and the simulated annealing algorithm

Jiang, Y Chen, Y Zeng, Z He, X

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 2008 IEEE 8th International Conference on Computer and Information Technology, CIT 2008, 2008, pp. 203 - 206
Issue Date:: 2008-09-22

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Field	Value	Language
dc.contributor.author	Jiang, Y	en_US
dc.contributor.author	Chen, Y	en_US
dc.contributor.author	Zeng, Z	en_US
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.date.issued	2008-09-22	en_US
dc.identifier.citation	Proceedings - 2008 IEEE 8th International Conference on Computer and Information Technology, CIT 2008, 2008, pp. 203 - 206	en_US
dc.identifier.isbn	9781424423583	en_US
dc.identifier.uri	http://hdl.handle.net/10453/10920
dc.description.abstract	C4.5 is a learning algorithm that adopts local search strategy, and it cannot obtain the best decision rules. On the other hand, the simulated annealing algorithm is a globally optimized algorithm and it avoids the drawbacks of C4.5. This paper proposes a new credit evaluation method based on decision tree and simulated annealing algorithm. The experimental results demonstrate that the proposed method is effective. © 2008 IEEE.	en_US
dc.relation.ispartof	Proceedings - 2008 IEEE 8th International Conference on Computer and Information Technology, CIT 2008	en_US
dc.relation.isbasedon	10.1109/CIT.2008.4594674	en_US
dc.title	A bank customer credit evaluation based on the decision tree and the simulated annealing algorithm	en_US
dc.type	Conference Proceeding
utslib.for	080106 Image Processing	en_US
utslib.for	080104 Computer Vision	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
dc.location.activity	Sydney, Australia	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

C4.5 is a learning algorithm that adopts local search strategy, and it cannot obtain the best decision rules. On the other hand, the simulated annealing algorithm is a globally optimized algorithm and it avoids the drawbacks of C4.5. This paper proposes a new credit evaluation method based on decision tree and simulated annealing algorithm. The experimental results demonstrate that the proposed method is effective. © 2008 IEEE.

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http://hdl.handle.net/10453/10920