Accelerating Prototype Selection with Spatial Abstraction

The growing adoption of machine learning techniques in recent years has led to an unprecedented demand for processing vast amounts of data, driving a tremendous increase in the consumption of computational resources and energy. Prototype selection techniques have been applied to reduce the requirements of computational resources and energy consumption that are needed by these techniques. In this paper, we propose an approach for speeding up existing prototype selection techniques. Firstly, it splits the dataset into a set of spatial partitions. Then, it builds a set of prototypes fo representing the information within each spatial partition. After, some conventional prototype selection algorithms can be applied to the prototypes selected by our approach. 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.