Meta-learning for evolutionary parameter optimization of classifiers |
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Authors: | Matthias Reif Faisal Shafait Andreas Dengel |
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Affiliation: | 1.German Research Center for Artificial Intelligence,Kaiserslautern,Germany |
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Abstract: | The performance of most of the classification algorithms on a particular dataset is highly dependent on the learning parameters
used for training them. Different approaches like grid search or genetic algorithms are frequently employed to find suitable
parameter values for a given dataset. Grid search has the advantage of finding more accurate solutions in general at the cost
of higher computation time. Genetic algorithms, on the other hand, are able to find good solutions in less time, but the accuracy
of these solutions is usually lower than those of grid search. |
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