Guo, LingRivero, DanielDorado, JuliánMunteanu, Cristian-RobertPazos, A.2024-10-302024-10-302011-08Guo, L., Rivero, D., Dorado, J., Munteanu, C. R., & Pazos, A. (2011). Automatic feature extraction using genetic programming: An application to epileptic EEG classification. Expert Systems with Applications, 38(8), 10425-10436. https://doi.org/10.1016/j.eswa.2011.02.1180957-41741873-6793http://hdl.handle.net/2183/39881This is the Author Accepted Manuscript; it is the version after peer review but before type setting, copy editing or publisher branding. The Version of Record is available online at https://doi.org/10.1016/j.eswa.2011.02.118[Abstract]: This paper applies genetic programming (GP) to perform automatic feature extraction from original feature database with the aim of improving the discriminatory performance of a classifier and reducing the input feature dimensionality at the same time. The tree structure of GP naturally represents the features, and a new function generated in this work automatically decides the number of the features extracted. In experiments on two common epileptic EEG detection problems, the classification accuracy on the GP-based features is significant higher than on the original features. Simultaneously, the dimension of the input features for the classifier is much smaller than that of the original features.engAtribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/Genetic programmingFeature extractionK-nearest neighbor classifier (KNN)Discrete wavelet transform (DWT)EpilepsyEEG classificationjournal articleopen access10.1016/j.eswa.2011.02.118