High training set size reduction by space partitioning and prototype abstraction

Instance-based learning methods like the nearest neighbour classiÿer generally suuer from the indiscriminate storage of all training instances, resulting in large memory requirements and slow execution speed. In this paper, new training set size reduction methods based on prototype generation and space partitioning are proposed. Experimental results show that the new algorithms achieve a very high reduction rate with still an important classiÿcation accuracy.