TY - GEN
T1 - A binary-encoded tabu-list genetic algorithm for fast support vector regression hyper-parameters tuning
AU - Gascón-Moreno, J.
AU - Salcedo-Sanz, S.
AU - Ortiz-García, E. G.
AU - Carro-Calvo, L.
AU - Saavedra-Moreno, B.
AU - Portilla-Figueras, J. A.
PY - 2011
Y1 - 2011
N2 - The selection of hyper-parameters in support vector machines for regression (SVMr) is an essential step in the training process of these learning machines. Unfortunately, there is not an exact method to obtain the optimal values of SVM hyper-parameters. Therefore, it is necessary to use a search algorithm in order to find the best set of hyper-parameters. Grid Search is the most commonly used option to perform such a hyper-parameters search, though other possibilities based on evolutionary computation algorithms have been proposed in the literature. In this paper we analyze the use of a standard genetic algorithm with binary encoding, which allows a fast exploration of the hyper-parameters space. We include a kind of tabu-list in the proposed algorithm, where we keep the last individuals generated by the genetic algorithm to avoid re-training of the SVMr with them. This technique allows a good improvement of the SVMr training time respect to the grid search approach, while keeping the machine accuracy almost unaltered.
AB - The selection of hyper-parameters in support vector machines for regression (SVMr) is an essential step in the training process of these learning machines. Unfortunately, there is not an exact method to obtain the optimal values of SVM hyper-parameters. Therefore, it is necessary to use a search algorithm in order to find the best set of hyper-parameters. Grid Search is the most commonly used option to perform such a hyper-parameters search, though other possibilities based on evolutionary computation algorithms have been proposed in the literature. In this paper we analyze the use of a standard genetic algorithm with binary encoding, which allows a fast exploration of the hyper-parameters space. We include a kind of tabu-list in the proposed algorithm, where we keep the last individuals generated by the genetic algorithm to avoid re-training of the SVMr with them. This technique allows a good improvement of the SVMr training time respect to the grid search approach, while keeping the machine accuracy almost unaltered.
KW - genetic algorithms
KW - hyper-parameters estimation
KW - Support Vector regression
KW - tabu-list
UR - http://www.scopus.com/inward/record.url?scp=84857613822&partnerID=8YFLogxK
U2 - 10.1109/ISDA.2011.6121831
DO - 10.1109/ISDA.2011.6121831
M3 - Conference contribution
AN - SCOPUS:84857613822
SN - 9781457716751
T3 - International Conference on Intelligent Systems Design and Applications, ISDA
SP - 1253
EP - 1257
BT - Proceedings of the 2011 11th International Conference on Intelligent Systems Design and Applications, ISDA'11
T2 - 2011 11th International Conference on Intelligent Systems Design and Applications, ISDA'11
Y2 - 22 November 2011 through 24 November 2011
ER -