A K-Farthest-Neighbor-based approach for support vector data description

Support vector data description (SVDD) is a well-known technique for one-class classification problems. However, it incurs high time complexity in handling large-scale datasets. In this paper, we propose a novel approach, named K-Farthest-Neighbor-based Concept Boundary Detection (KFN-CBD), to improve the training efficiency of SVDD. KFN-CBD aims at identifying the examples lying close to the boundary of the target class, and these examples, instead of the entire dataset, are then used to learn the classifier. Extensive experiments have shown that KFN-CBD obtains substantial speedup compared to standard SVDD, and meanwhile maintains comparable accuracy as the entire dataset used.