TY - GEN
T1 - Classification rule mining for automatic credit approval using genetic programming
AU - Sinclair, Mark
AU - Sakprasat, Sum
PY - 2007/9
Y1 - 2007/9
N2 - Automatic credit approval is important for the efficient processing of credit applications. Eight different genetic programming (GP) approaches for the classification rule mining of a credit card application dataset are investigated, using both a Booleanizing technique and strongly- typed GP. In addition, the use of GP for missing value handling is evaluated. Overall, on the Australian Credit Approval dataset, those GP approaches that had poorer classification correctness on the training data often proved better at generalizing for the test set.
AB - Automatic credit approval is important for the efficient processing of credit applications. Eight different genetic programming (GP) approaches for the classification rule mining of a credit card application dataset are investigated, using both a Booleanizing technique and strongly- typed GP. In addition, the use of GP for missing value handling is evaluated. Overall, on the Australian Credit Approval dataset, those GP approaches that had poorer classification correctness on the training data often proved better at generalizing for the test set.
UR - https://www.scopus.com/pages/publications/70249126803
U2 - 10.1109/CEC.2007.4424518
DO - 10.1109/CEC.2007.4424518
M3 - Conference contribution
SN - 978-1-4244-1339-3
BT - 2007 IEEE Congress on Evolutionary Computation
ER -