Use of neighborhood and stratification approaches to speed up instance selection algorithm

This paper investigates a method for instance selection in the context of supervised classification adapted to large databases. Based on the scale up concept, the method reduces the time required to perform the selection procedure by enabling the application of known condensation instance techniques to only small data sets instead of the whole set. The novelty of our approach relies in the way of hybridizing neighborhood and stratification approaches. The key idea is to consider instances found out for a given strata to generate sub populations for the other strata representing critical regions of the feature space. Experiments performed with various data sets revealed the effectiveness and applicability of the proposed approach.