Hybrid multi-agent system for metalearning in data mining
Klára Pešková, Jakub Šmíd, Martin Pilát, Ondřej Kazı́k, Roman Neruda · ASEP · 2014
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.....