Computational time reduction for credit scoring: An integrated approach based on support vector machine and stratified sampling method

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Hens20126774,
  author =       "Akhil Bandhu Hens and Manoj Kumar Tiwari",
  title =        "Computational time reduction for credit scoring: An
                 integrated approach based on support vector machine and
                 stratified sampling method",
  journal =      "Expert Systems with Applications",
  volume =       "39",
  number =       "8",
  pages =        "6774--6781",
  year =         "2012",
  ISSN =         "0957-4174",
  DOI =          "doi:10.1016/j.eswa.2011.12.057",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0957417411017283",
  keywords =     "genetic algorithms, genetic programming, Support
                 vector machine, Credit scoring, F score, Stratified
                 sampling",
  abstract =     "With the rapid growth of credit industry, credit
                 scoring model has a great significance to issue a
                 credit card to the applicant with a minimum risk. So
                 credit scoring is very important in financial firm like
                 bans etc. With the previous data, a model is
                 established. From that model is decision is taken
                 whether he will be granted for issuing loans, credit
                 cards or he will be rejected. There are several
                 methodologies to construct credit scoring model i.e.
                 neural network model, statistical classification
                 techniques, genetic programming, support vector model
                 etc. Computational time for running a model has a great
                 importance in the 21st century. The algorithms or
                 models with less computational time are more efficient
                 and thus gives more profit to the banks or firms. In
                 this study, we proposed a new strategy to reduce the
                 computational time for credit scoring. In this approach
                 we have used SVM incorporated with the concept of
                 reduction of features using F score and taking a sample
                 instead of taking the whole dataset to create the
                 credit scoring model. We run our method two real
                 dataset to see the performance of the new method. We
                 have compared the result of the new method with the
                 result obtained from other well known method. It is
                 shown that new method for credit scoring model is very
                 much competitive to other method in the view of its
                 accuracy as well as new method has a less computational
                 time than the other methods.",
}

Genetic Programming entries for Akhil Bandhu Hens Manoj Kumar Tiwari

Citations