Efficient Support Vector Machine Classification Using Prototype Selection and Generation

Abstract

Although Support Vector Machines (SVMs) are considered effective supervised learning methods, their training procedure is time-consuming and has high memory requirements. Therefore, SVMs are inappropriate for large datasets. Many Data Reduction Techniques have been proposed in the context of dealing with the drawbacks of k-Nearest Neighbor classification. This paper adopts the concept of data reduction in order to cope with the high computational cost and memory requirements in the training process of SVMs. Experimental results illustrate that Data Reduction Techniques can effectively improve the performance of SVMs when applied as a preprocessing step on the training data.