@article{MAKHILLIJSC201813421451,
    title = {Comparative Study of Kernel Function for Support Vector Machine on
Financial Dataset},
    journal = {International Journal of Soft Computing},
    volume = {13},
    number = {4},
    pages = {129-133},
    year = {2018},
    issn = {1816-9503},
    doi = {ijscomp.2018.129.133},
    url = {https://makhillpublications.co/view-article.php?issn=1816-9503&doi=ijscomp.2018.129.133},
    author = {Noviyanti and},
    keywords = {support vector machine,kernel function,Financial distress,polynomial,SVM Model,accuracy},
    abstract = {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.}
    }