Abstract
A genetic approach is presented in this article to deal with two problems: a) feature selection and b) the determination of parameters in Support Vector Regression (SVR). We consider a kind of genetic algorithm (GA) in which the probabilities of mutation and crossover are determined in the evolutionary process. Some empirical experiments are made to measure the efficiency of this algorithm against two frequently used approaches.
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Mejía-Guevara, I., Kuri-Morales, Á. (2007). Evolutionary Feature and Parameter Selection in Support Vector Regression. In: Gelbukh, A., Kuri Morales, Á.F. (eds) MICAI 2007: Advances in Artificial Intelligence. MICAI 2007. Lecture Notes in Computer Science(), vol 4827. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76631-5_38
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DOI: https://doi.org/10.1007/978-3-540-76631-5_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76630-8
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