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This paper describes an efficient method to construct reliable machine learning applications in peer-to-peer (P2P) networks by building ensemble based meta methods. We consider this problem in the context of distributed Web exploration applications like focused crawling. Typical applications are user-specific classification of retrieved Web contents into personalized topic hierarchies as well as automatic refinements of such taxonomies using unsupervised machine learning methods (e.g. clustering). Our approach is to combine models from multiple peers and to construct the advanced decision model that takes the generalization performance of multiple ‘local' peer models into account. In addition, meta algorithms can be applied in a restrictive manner, i.e. by leaving out some ‘uncertain' documents. The results of our systematic evaluation show the viability of the proposed approach.