LAUSR

Abstract Support vector machine (SVM) is a popular machine learning method with a solid theoretical foundation, and has shown promising performance on different classification problems. However, it suffers from an expensive training cost, which makes it not be very suitable for the application with a large-scale training set. To this end, as a data pre-processing technique, training set selection (TSS) for SVM has received much attention recently, since it can reduce the size of SVM training set without degrading the performance. In this paper, a subregion division based multi-objective evolutionary algorithm termed SDMOEA-TSS is proposed for SVM training set selection, where objective space is divided into several subregions for effectively searching good solutions. Specifically, in SDMOEA-TSS, a divided based initialization strategy is firstly suggested to initialize the population to locate in different regions of objective space. Then a subregion based evolutionary (including crossover, mutation and update) strategy is developed, which not only makes full use of individuals in each subregion for local search but also maintains the whole population’s global search ability. Empirical studies on 21 public data sets demonstrate the superiority of the proposed algorithm over the state-of-the-arts in terms of both quality and diversity of the selected SVM training subset.