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Cited by 271

The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.

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

Open access
Guo, Ling, Rivero, Daniel, Dorado, Julián, Munteanu, Cristian R., Pazos, Alejandro·2011·Expert Systems with Applications

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.

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Crossref · 10.1016/j.eswa.2011.02.118

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Metadata

Authors
Guo, L., Rivero, D., Dorado, J., Munteanu, C. R., & Pazos, A.
Venue
Expert Systems with Applications
Publisher
Elsevier BV
Published
2011-08
Volume
38
Issue
8
Pages
10425-10436
ISSN
0957-4174 (ISSN-L)
OpenAlex
W1988150589
Cited by
271
References
40
FWCI
12.12
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Automatic feature extraction using genetic programming: An application to epileptic EEG classification. https://app.cochat.ai/papers/10.1016/j.eswa.2011.02.118
The paper
Guo, 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. https://doi.org/10.1016/j.eswa.2011.02.118

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.