Using Genetic Programming to Estimate Performance of Computational Intelligence Models

0425344 - ÚI 2014 RIV DE eng C - Konferenční příspěvek (zahraniční konf.)
Šmíd, J. - Neruda, Roman
Using Genetic Programming to Estimate Performance of Computational Intelligence Models.
Adaptive and Natural Computing Algorithms. Berlin: Springer, 2013 - (Tomassini, M.; Antonioni, A.; Daolio, F.; Buesser, P.), s. 169-178. Lecture Notes in Computer Science, 7824. ISBN 978-3-642-37212-4. ISSN 0302-9743.
[ICANNGA'2013 /11./. Lausanne (CH), 04.04.2013-06.04.2013]
Grant CEP: GA ČR GAP202/11/1368
Grant ostatní: UK(CZ) SVV 2673/4
Institucionální podpora: RVO:67985807
Klíčová slova: metalearning * genetic programming * data mining * performance prediction
Kód oboru RIV: IN - Informatika

This paper deals with the problem of choosing the most suitable model for a new data mining task. The metric is proposed on the data mining tasks space, and similar tasks are identified based on this metric. A function estimating models performance on the new task from both the time and error point of view is evolved by means of genetic programming. The approach is verified on data containing results of several hundred thousands machine learning experiments.
Trvalý link: http://hdl.handle.net/11104/0231237