Tuning the Architecture of Support Vector Machine: The Case of Bankruptcy Prediction

초록

Tuning the architecture of SVM (support vector machine) is to build an SVM model of better perfor-mance. Two different tuning methods of the grid search and the GA (genetic algorithm) have been addressed in the literature, each of which has its own methodological pros and cons. This paper sug-gests a combined method for tuning the architecture of SVM models, which employs the GAM (ge-neralized additive models), the grid search, and the GA in sequence. The GAM is used for selecting input variables, and the grid search and the GA are employed for finding optimal parameter values of the SVM models. Applying the method to a bankruptcy prediction problem, we show that SVM model tuned by the proposed method outperforms other SVM models.

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