Authors: Abdelhamid Djeffal; Mohemed Chaouki Babahenini; Abdelmalik Taleb-Ahmed
Addresses: Department of Computer Science, LESIA Laboratory, Biskra University, BP 145, 07000 Biskra, Algeria ' Department of Computer Science, LESIA Laboratory, Biskra University, BP 145, 07000 Biskra, Algeria ' LAMIH Laboratory, University of Valencinnes, 59313, Valenciennes, cedex 9, France
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.
Keywords: support vector machines; binary SVM; sample reduction; fast training; support vectors; separating hyperplane; separating margin; decision boundaries; statistical learning; machine learning.
International Journal of Data Mining, Modelling and Management, 2017 Vol.9 No.1, pp.1 - 16
Received: 07 Feb 2015
Accepted: 08 Jan 2016
Published online: 18 Feb 2017 *