Fast binary support vector machine learning method by samples reduction

Abstract

Support vector machine is a well-known method of statistical learning by its good accuracy; however, its training time is very poor especially in case of huge databases. Many research works aim to reduce training samples to improve training time without significant loss in accuracy. In this paper, we propose a method called CB-SR, based on filtering and revision stages to eliminate samples that have little influence on learning results. Filtering stage uses a covering-based principle of samples to eliminate those faraway from decision boundaries and keep the closest ones. Revision stage allows to add after the first learning, samples eventually discarded by mistake. The results we obtain show the benefits of our approach over others existing ones.