Prototype Selection from Homogeneous Subsets by a Monte Carlo Sampling

May 1998

Proceedings of the Eleventh International Florida Artificial Intelligence Research Society Conference (FLAIRS 1998)

Volume

Proceedings of the Eleventh International Florida Artificial Intelligence Research Society Conference (FLAIRS 1998)

All Papers

In order to reduce computation and storage costs of learning methods, we present a prototype selection algorithm. This approach uses information contained in a connected neighborhood graph. It determines the humber of homogenous subsets in the R space, and uses it to fix the number of prototypes in advance. Once this number is determined, we identify prototypes applying a stratified Monte Carlo sampling algorithm. We present an application of our algorithm on a simulated example, comparing results with those obtained with other methods.