Genetic Support Vector Classification and Feature Selection
Iván Mejía-Guevara, Ángel Kuri-Morales · 2008
An important issue regarding the design of Support Vector Machines (SVMs) is considered in this article, namely, the fine tuning of parameters in SVMs. This problem is tackled by using a self-adaptive Genetic Algorithm (GA). The same GA is used for feature selection. We validate our results implementing some statistical tests based on single domain benchmark data sets, which are used for comparison with other traditional methods. One of these methods is commonly used for the selection of parameters in SVMs.