Hybrid Multi-Agent System for Metalearning in Data Mining

نویسندگان

  • Klára Pesková
  • Jakub Smíd
  • Martin Pilát
  • Ondrej Kazík
  • Roman Neruda
چکیده

In this paper, a multi-agent system for metalearning in the data mining domain is presented. The system provides a user with intelligent features, such as recommendation of suitable data mining techniques for a new dataset, parameter tuning of such techniques, and building up a metaknowledge base. The architecture of the system, together with different user scenarios, and the way they are handled by the system, are described.

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تاریخ انتشار 2014