TENFOLD BOOTSTRAP PROCEDURE FOR SUPPORT VECTOR MACHINES
AbstractCross validation is often used to split input data into training and test set in Support vector machines. The two most commonly used cross validation versions are the tenfold and leave-one-out cross validation. Another commonly used resampling method is the random test/train split. The advantage of these methods is that they avoid overﬁtting in the model and perform model selection. They, however, can increase the computational time for ﬁtting Support vector machines with the increase of the size of the dataset. In this research, we propose an alternative for ﬁtting SVM, which we call the tenfold bootstrap for Support vector machines. This resampling procedure can signiﬁcantly reduce execution time despite the big number of observations, while preserving 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.
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How to Cite
Vrigazova, B., & Ivanov, I. (2020). TENFOLD BOOTSTRAP PROCEDURE FOR SUPPORT VECTOR MACHINES. Computer Science, 21(2). https://doi.org/10.7494/csci.2020.21.2.3634