Tenfold bootstrap procedure for support vector machines

Abstract

Cross validation is often used to split input data into training and test sets in support vector machines. The two most commonly used cross validation versions are tenfold and leave-one-out cross validation. Another commonly used resampling method is random test/train split. The advantage of these methods is that they avoid overfitting in a model and perform model selection. However, they can increase the computational time for fitting support vector machines by increasing the size of the dataset. In this research, we propose an alternative for fitting SVM, which we call tenfold bootstrap for support vector machines. This resampling procedure can significantly reduce execution time despite the large number of observations while preserving a model's accuracy. With this finding, we propose a solution to the problem of slow execution time when fitting support vector machines on big datasets.