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Unformatted text preview: models since they are essential for the risk management of financial
institutions. Thus, researchers have applied various data- driven approaches to enhance prediction performance including statistical and artificial intelligence techniques.
Recently, support vector machines(SVMs) are becoming popular because they use a risk function consisting of the empirical error and a regularized term which is derived
from the structural risk minimization principle. In addition, they don’t require huge training samples and have little possibility of overfitting. However, in order to use SVM, a
user should determine several factors such as the parameters of a kernel function, appropriate feature subset, and proper instance subset by heuristics, which hinders
accurate prediction results when using SVM.
There is a paper [ (http://zoe.bme.gatech.edu/~klee7/docs/iconip_Simultaneous_Opt_of_SVM_GA_for_data_prediction.pdf) ]proposing a novel approach to enhance
the prediction performance of SVM for the prediction of financial distress. Their suggestion is the simultaneous optimization of the feature selection and the instance selection
as well as the parameters of a kernel function for SVM by using genetic algorithms (GAs). They apply their model to a real- world case. Experimental results show that the
prediction accuracy of conventional SVM may be improved significantly by using their model.
Exte ns ion: Finding Optimal Parame te r Value s
The accuracy of an SVM model dependents on the selection of the model parameters. DTREG provides two methods for finding optimal parameter values. Grid search:
tries values of each parameter across the specified search range using geometric steps. Pattern search/compass search/line search:starts at the center of the search range and
makes trial steps in each direction for each parameter.
If the fit of the model improves, the search center moves to the new point and the process is repeated. If no improvement is found, the step size is reduced and the search is
tried again. The pattern sear...
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- Winter '13