Classification rule mining for automatic credit approval using genetic programming

@inproceedings{Sakprasat2008, abstract = {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.}, author = {Sakprasat, S. and Sinclair, M. C. }, booktitle = {Evolutionary Computation, 2007. CEC 2007. IEEE Congress on}, citeulike-article-id = {2801473}, doi = {http://dx.doi.org/10.1109/CEC.2007.4424518}, journal = {Evolutionary Computation, 2007. CEC 2007. IEEE Congress on}, pages = {548--555}, posted-at = {2008-05-15 13:17:52}, priority = {2}, title = {Classification rule mining for automatic credit approval using genetic programming}, url = {http://dx.doi.org/10.1109/CEC.2007.4424518}, year = {2007} }

TY - CONF ID - Sakprasat2008 L3 - citeulike-article-id:2801473 TI - Classification rule mining for automatic credit approval using genetic programming T2 - Evolutionary Computation, 2007. CEC 2007. IEEE Congress on JF - Evolutionary Computation, 2007. CEC 2007. IEEE Congress on SP - 548 EP - 555 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. AU - Sakprasat, S AU - Sinclair, MC PY - 2007/// UR - http://dx.doi.org/10.1109/CEC.2007.4424518 ER -