Title: MALEF: Framework for distributed machine learning and data mining

Authors: Jan Tozicka, Michael Rovatsos, Michal Pechoucek, Stepan Urban

Addresses: Gerstner Laboratory, Czech Technical University, Technicka 2, Prague, 166 27, Czech Republic. ' School of Informatics, The University of Edinburgh, Edinburgh EH8 9LE, UK. ' Gerstner Laboratory, Czech Technical University, Technicka 2, Prague, 166 27, Czech Republic. ' Gerstner Laboratory, Czech Technical University, Technicka 2, Prague, 166 27, Czech Republic

Abstract: Growing importance of distributed data mining techniques has recently attracted attention of researchers in multiagent domain. In this paper we present a novel framework MultiAgent Learning Framework (MALEF) designed for both the agent-based distributed machine learning as well as data mining. Proposed framework is based on: the exchange of meta-level descriptions of individual learning process; online reasoning about learning success and learning progress. This paper illustrates how MALEF framework can be used in practical system in which different learners use different datasets, hypotheses and learning algorithms. We describe our experimental results obtained using this system and review related work on the subject.

Keywords: multiagent learning frameworks; distributed machine learning; architectures; unsupervised clustering; market-based approaches; data mining; multi-agent systems; MAS; agent-based systems.

DOI: 10.1504/IJIIDS.2008.017242

International Journal of Intelligent Information and Database Systems, 2008 Vol.2 No.1, pp.6 - 24

Published online: 20 Feb 2008 *

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