An efficient instance selection algorithm to reconstruct training set for support vector machine

Support vector machine is a classification model which has been widely used in many nonlinear and high dimensional pattern recognition problems. However, it is inefficient or impracticable to implement support vector machine in dealing with large scale training set due to its computational difficulties as well as the model complexity. In this paper, we study the support vector recognition problem mainly in the context of the reduction methods to reconstruct training set for support vector machine. We focus on the fact of uneven distribution of instances in the vector space to propose an efficient self-adaption instance selection algorithm from the viewpoint of geometry-based method. Also, we conduct an experimental study involving eleven different sizes of datasets from UCI repository for measuring the performance of the proposed algorithm as well as six competitive instance selection algorithms in terms of accuracy, reduction capabilities, and runtime. The extensive experimental results show that the proposed algorithm outperforms most of competitive algorithms due to its high efficiency and efficacy.