Expediting model selection for support vector machines based on an advanced data reduction algorithm

In recent years, the support vector machine (SVM) has been extensively applied to deal with various data classification problems. However, it has also been observed that, for some datasets, the classification accuracy delivered by the SVM is very sensitive to how the cost parameter and the kernel parameters are set. As a result, the user may need to conduct extensive cross validation in order to figure out the optimal parameter setting. How to expedite the model selection process of the SVM has attracted a high degree of attention in the machine learning research community in recent years. This paper proposes an advanced data reduction algorithm aimed at expediting the model selection process of the SVM. Experimental results reveal that the proposed mechanism is able to deliver a speedup of over 70 times without causing meaningful side effects and compares favorably with the alternative approaches.