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Combining Parameter Space Search and Meta-learning for Data-Dependent Computational Agent Recommendation

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4 Author(s)
Ondrej Kazík ; Dept. of Theor. Comput. Sci., Charles Univ. Prague, Prague, Czech Republic ; Klára Peková ; Martin Pilát ; Roman Neruda

The goal of our data-mining multi-agent system is to facilitate data-mining experiments without the necessary knowledge of the most suitable machine learning method and its parameters to the data. In order to replace the experts knowledge, the meta-learning subsystems are proposed including the parameter-space search and method recommendation based on previous experiments. In this paper we show the results of the parameter-space search with several search algorithms - tabulation, random search, simmulated annealing, and genetic algorithm.

Published in:

Machine Learning and Applications (ICMLA), 2012 11th International Conference on  (Volume:2 )

Date of Conference:

12-15 Dec. 2012