id: 05856830
dt: j
an: 05856830
au: Chen, Weimin; Ma, Chaoqun; Ma, Lin
ti: Mining the customer credit using hybrid support vector machine technique.
so: Expert Syst. Appl. 36, No. 4, 7611-7616 (2009).
py: 2009
pu: Elsevier Science (Pergamon), Amsterdam
la: EN
cc: 
ut: credit scoring; support vector machines; classification and regression
    tree; multivariate adaptive regression splines; credit-risk evaluation
ci: 
li: doi:10.1016/j.eswa.2008.09.054
ab: Summary: Credit scoring has become a critical and challenging management
    science issue, as the credit industry has been facing fiercer
    competition in recent years. Many methods have been suggested to tackle
    this problem in the literature. In this paper, we proposed hybrid
    support vector machine technique based on three strategies: (1) using
    CART to select input features, (2) using MARS to select input features,
    (3) using grid search to optimize model parameters. In order to verify
    the feasibility and effectiveness of the proposed hybrid SVM model, one
    credit card dataset provided by a local bank in China is used in this
    study. Analytic results demonstrate that the hybrid SVM technique not
    only has the best classification rate, but also has the lowest Type II
    error in comparison with CART, MARS and SVM and justify the
    presumptions that SVM having better capability of capturing nonlinear
    relationship among variables.
rv: