Ant Colony Optimization, Genetic Programming and a hybrid approach for credit scoring: A comparative study
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- @InProceedings{Aliehyaei:2014:SKIMA,
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author = "R. Aliehyaei and S. Khan",
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booktitle = "8th International Conference on Software, Knowledge,
Information Management and Applications (SKIMA)",
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title = "Ant Colony Optimization, Genetic Programming and a
hybrid approach for credit scoring: A comparative
study",
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year = "2014",
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abstract = "Credit scoring is a commonly used method for
evaluating the risk involved in granting credits. Both
Genetic Programming (GP) and Ant Colony Optimisation
(ACO) have been investigated in the past as possible
tools for credit scoring. This paper reports an
investigation into the relative performances of GP, ACO
and a new hybrid GP-ACO approach, which relies on the
ACO technique to produce the initial populations for
the GP technique. Performance of the hybrid approach
has been compared with both the GP and ACO approaches
using two well-known benchmark data sets. Experimental
results demonstrate the dependence of GP and ACO
classification accuracies on the input data set. For
any given data set, the hybrid approach performs better
than the worse of the other two methods. Results also
show that use of ACO in the hybrid approach has only a
limited impact in improving GP performance.",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/SKIMA.2014.7083391",
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month = dec,
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notes = "Also known as \cite{7083391}",
- }
Genetic Programming entries for
R Aliehyaei
S Khan
Citations