TY  - JOUR
T1  - Comparative Study of Kernel Function for Support Vector Machine on
Financial Dataset
AU - Santoso, Noviyanti AU - Wibowo, Wahyu 
JO  - International Journal of Soft Computing
VL  - 13
IS  - 4
SP  - 129
EP  - 133
PY  - 2018
DA  - 2001/08/19
SN  - 1816-9503
DO  - ijscomp.2018.129.133
UR  - https://makhillpublications.co/view-article.php?doi=ijscomp.2018.129.133
KW  - support vector machine
KW  -kernel function
KW  -Financial distress
KW  -polynomial
KW  -SVM Model
KW  -accuracy
AB  - Due to the increasing number of business failures effect from economic crisis, it is challenging to
develop a financial distress prediction model. The prediction model is the early warning system that has any
advantages for companies, consumer, creditors investors and the economy of country in general. We develop
SVM Model with different kernel function such as linear, polynomial and radial basis function. We purposed
tuning method with 10-fold cross validation to find the best pair of parameters for each kernel function. The
result shows that SVM model using radial basis kernel with optimal parameter C = 5 and γ = 1 is obtain
appropriate accuracy, the AUC value is 0.72.
ER  -