Geodesic
A simple upper bound to the Bayes error probability for feature selection
Kybernetika, Tome 34 (1998) no. 4, p. [387].

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In this paper, feature selection in multiclass cases for classification of remote-sensing images is addressed. A criterion based on a simple upper bound to the error probability of the Bayes classifier for the minimum error is proposed. This criterion has the advantage of selecting features having a link with the error probability with a low computational load. Experiments have been carried out in order to compare the performances provided by the proposed criterion with the ones of some of the widely used feature-selection criteria presented in the remote-sensing literature. These experiments confirm the effectiveness of the proposed criterion, which performs slightly better than all the others considered in the paper.
Classification : 62H30, 68G10, 68T10
Keywords: Bayes error probability; feature selection 
@article{KYB_1998__34_4_a5,
     author = {Bruzzone, Lorenzo and Serpico, Sebastiano B.},
     title = {A simple upper bound to the {Bayes} error probability for feature selection},
     journal = {Kybernetika},
     pages = {[387]},
     publisher = {mathdoc},
     volume = {34},
     number = {4},
     year = {1998},
     zbl = {1274.68377},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/KYB_1998__34_4_a5/}
}
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Bruzzone, Lorenzo; Serpico, Sebastiano B. A simple upper bound to the Bayes error probability for feature selection. Kybernetika, Tome 34 (1998) no. 4, p. [387]. http://geodesic.mathdoc.fr/item/KYB_1998__34_4_a5/