Prototype selection is one of the typical goals of machine learning, which aims to reduce the number of vectors in the training set. The local set border selector (LSBo) algorithm is presented as a Pareto optimal choice between the reduction power of the training set and the classification quality. Its complexity is \(O(n^2)\) , which means that it is not very advantageous for larger sets. This article presents the Fast LSBo algorithm, which is based on the original idea of the LSBo algorithm. After the applied conceptual changes, the algorithm has achieved a complexity of \(O(m \log m)\) . Additionally, the analysis of Fast LSBo on several data sets shows that its classification quality and reduction power remain statistically indistinguishable from the original LSBo algorithm.