Support vector candidates selection via Delaunay graph and convex-hull for large and high-dimensional datasets

•Accelerating support vector machine training.•Support vector candidates selection based on computer geometry.•Dimensionality reduction methods influence the support vector candidates selection.•The proposed method work in a reduced dimension.•SVM accuracy is not compromised when trained with proposed method. We propose a method to pre-select support vector (SV) candidates for training support vector machines (SVM) with a large-scale dataset. The technique creates a support vector candidates set to feed the SVM training phase, where this set is built by rescaling the dataset to three dimensions, if necessary, and creating a Delaunay Graph and a convex-hull (CH) for each class. The SV candidates set is formed by picking the points from all CHs, and its neighbors from the Delaunay graph, even in a reduced dimension. By testing the technique in four datasets with different size and feature number, we demonstrate that the proposed method accelerates SVM training process without degrading accuracy proportionally to the difference between original dataset and SV candidates set dimensions.