Evolutionary Optimization of Meta Data Metric for Method Recommendation

0425750 - ÚI 2015 RIV US eng C - Konferenční příspěvek (zahraniční konf.)
Kazík, O. - Šmíd, J. - Neruda, Roman
Evolutionary Optimization of Meta Data Metric for Method Recommendation.
Proceedings of the 2013 IEEE Conference on Cybernetics and Intelligent Systems (CIS). Piscataway: IEEE Computer Society, 2013, s. 123-127. ISBN 978-1-4799-1072-4.
[CIS-RAM 2013. IEEE International Conference on Cybernetics and Intelligent Systems /6./ and IEEE International Conference on Robotics, Automation and Mechatronics /6./. Manila (PH), 12.11.2013-15.11.2013]
Grant CEP: GA ČR GAP202/11/1368
Institucionální podpora: RVO:67985807
Klíčová slova: evolutionary algorithms * data mining * metalearning
Kód oboru RIV: IN - Informatika

Metalearning - a method for recommendation the most suitable data-mining algorithm to an unknown dataset - is an important problem that needs to be solved in order to design a completely autonomous data-mining solver. This paper deals with this particular problem by proposing a machinelearning method which recommends the most suitable algorithm to an unknown dataset based on the results of previous datamining experiments. The fundamental idea behind this is that the algorithms will perform similarly on similar datasets. The choice of datasets features - called meta data - is presented and the metric comparing datasets is optimized by means of evolutionary computation.
Trvalý link: http://hdl.handle.net/11104/0232847