An evolutionary ensemble-based method for rule extraction with distributed data

Abstract

This paper presents a methodology for knowledge discovery from inherently distributed data without moving it from its original location, completely or partially, to other locations for legal or competition issues. It is based on a novel technique that performs in two stages: first, discovering the knowledge locally and second, merging the distributed knowledge acquired in every location in a common privacy aware maximizing the global accuracy by using evolutionary models. The knowledge obtained in this way improves the one achieved in the local stores, thus it is of interest for the concerned organizations. © 2009 Springer Berlin Heidelberg.