Automatic feature extraction using genetic programming: An application to epileptic EEG classification

Authors:
Ling Guo;Daniel Rivero;Julián Dorado;Cristian R. Munteanu;Alejandro Pazos
Affiliations:
Department of Information Technologies and Communications, University of La Coruña, Campus Elviña, 15071 A Coruña, Spain;Department of Information Technologies and Communications, University of La Coruña, Campus Elviña, 15071 A Coruña, Spain;Department of Information Technologies and Communications, University of La Coruña, Campus Elviña, 15071 A Coruña, Spain;Department of Information Technologies and Communications, University of La Coruña, Campus Elviña, 15071 A Coruña, Spain;Department of Information Technologies and Communications, University of La Coruña, Campus Elviña, 15071 A Coruña, Spain
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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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.