Abstract
We study the problem of predictive data mining in a competitive multi-agent setting, in which each agent is assumed to have some partial knowledge required for correctly classifying a set of unlabelled examples. The agents are self-interested and therefore need to reason about the trade-offs between increasing their classification accuracy by collaborating with other agents and disclosing their private classification knowledge to other agents through such collaboration. We analyze the problem and propose a set of components which can enable cooperation in this otherwise competitive task. These components include measures for quantifying private knowledge disclosure, data-mining models suitable for multi-agent predictive data mining, and a set of strategies by which agents can improve their classification accuracy through collaboration. The overall framework and its individual components are validated on a synthetic experimental domain.
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Lisý, V., Jakob, M., Benda, P., Urban, Š., Pěchouček, M. (2009). Towards Cooperative Predictive Data Mining in Competitive Environments. In: Cao, L., Gorodetsky, V., Liu, J., Weiss, G., Yu, P.S. (eds) Agents and Data Mining Interaction. ADMI 2009. Lecture Notes in Computer Science(), vol 5680. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03603-3_8
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DOI: https://doi.org/10.1007/978-3-642-03603-3_8
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