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Actualmente, en diferentes ciencias como la medicina, las geociencias, la astronomía, entre otras, la tarea de clasificación supervisada ha dado solución a muchos problemas importantes. Uno de los algoritmos de clasificación supervisada más utilizados ha sido k vecinos más cercanos (o k Neares Neighbors, k-NN), el cual ha mostrado ser un algoritmo simple, pero efectivo. El algoritmo k vecinos más cercanos realiza una comparación exhaustiva entre el nuevo objeto a clasificar y todos los elementos del conjunto de entrenamiento. Sin embargo, cuando el conjunto de entrenamiento es grande, este proceso es costoso y en algunos casos esta búsqueda exhaustiva se vuelve un proceso muy lento o inaplicable. Para agilizar el proceso de clasificación y omitir comparaciones, se han propuesto en los últimos años clasificadores rápidos basados en el algoritmo del vecino más cercano (Fast k-NN). La mayoría de estos algoritmos Fast k-NN se basan en las propiedades métricas de la función de distancia para omitir comparaciones o bien otras heurísticas.