- author = "Sum Sakprasat and Mark C. Sinclair",
- title = "Classification Rule Mining for Automatic Credit Approval Using Genetic Programming",
- booktitle = "2007 IEEE Congress on Evolutionary Computation",
- year = "2007",
- editor = "Dipti Srinivasan and Lipo Wang",
- pages = "548--555",
- address = "Singapore",
- month = "25-28 " # sep,
- organization = "IEEE Computational Intelligence Society",
- publisher = "IEEE Press",
- ISBN = "1-4244-1340-0",
- file = "1223.pdf",
- keywords = "genetic algorithms, genetic programming",
-
DOI = "
doi:10.1109/CEC.2007.4424518",
- 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 generalising for the test set.",
-
notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and
the IET.
IEEE Catalog Number: 07TH8963C",
