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Automatic feature extraction using genetic programming: An application to epileptic EEG classification
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DOI：10.1016/j.eswa.2011.02.118.png)
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Abstract
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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. (C) 2011 Elsevier Ltd. All rights reserved.
Keywords:
Genetic programming
Feature extraction
K-nearest neighbor classifier (KNN)
Discrete wavelet transform (DWT)
Epilepsy
EEG classification
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