Similarity Based Distributed Classification

Most distributed knowledge discovery approaches view data distribution as a technical issue and combine local models aiming at a single global model. This however, is unsuitable for inherently distributed databases, which often produce models that differ semantically. In this paper we present an approach for distributed classification that uses the pairwise similarity of local models in order to produce a better model for each of the distributed databases. This is achieved by averaging the decisions of all local models weighted by their similarity with the model induced from the origin of the unlabelled data.