  The increasing digitalization in industry and society leads to a growing
abundance of data available to be processed and exploited. However, the high
volume of data requires considerable computational resources for applying
machine learning approaches. Prototype selection techniques have been applied
to reduce the requirements of computational resources that are needed by these
techniques. In this paper, we propose an approach for speeding up existing
prototype selection techniques. It builds an abstract representation of the
dataset, using the notion of spatial partition. The second step uses this
abstract representation to prune the search space efficiently and select a set
of candidate prototypes. After, some conventional prototype selection
algorithms can be applied to the candidates selected by our approach. Our
approach was integrated with five conventional prototype selection algorithms
and tested on 14 widely recognized datasets used in classification tasks. The
performance of the modified algorithms was compared to that of their original
versions in terms of accuracy and reduction rate. The experimental results
demonstrate that, overall, our proposed approach maintains accuracy while
enhancing the reduction rate of the original prototype selection algorithms and
simultaneously reducing their execution times.
