A simple upper bound to the Bayes error probability for feature selection
Lorenzo Bruzzone; Sebastiano B. Serpico
Kybernetika (1998)
- Volume: 34, Issue: 4, page [387]-392
- ISSN: 0023-5954
Access Full Article
top
Abstract
topHow to cite
topBruzzone, Lorenzo, and Serpico, Sebastiano B.. "A simple upper bound to the Bayes error probability for feature selection." Kybernetika 34.4 (1998): [387]-392. <http://eudml.org/doc/33366>.
@article{Bruzzone1998,
abstract = {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.},
author = {Bruzzone, Lorenzo, Serpico, Sebastiano B.},
journal = {Kybernetika},
keywords = {Bayes error probability; feature selection; Bayes error probability; feature selection},
language = {eng},
number = {4},
pages = {[387]-392},
publisher = {Institute of Information Theory and Automation AS CR},
title = {A simple upper bound to the Bayes error probability for feature selection},
url = {http://eudml.org/doc/33366},
volume = {34},
year = {1998},
}
TY - JOUR
AU - Bruzzone, Lorenzo
AU - Serpico, Sebastiano B.
TI - A simple upper bound to the Bayes error probability for feature selection
JO - Kybernetika
PY - 1998
PB - Institute of Information Theory and Automation AS CR
VL - 34
IS - 4
SP - [387]
EP - 392
AB - 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.
LA - eng
KW - Bayes error probability; feature selection; Bayes error probability; feature selection
UR - http://eudml.org/doc/33366
ER -
References
top- Bruzzone L., Roli F., Serpico S. B., 10.1109/36.477187, IEEE Trans. Geoscience Remote Sensing 33 (1995), 6, 1318–1321 (1995) DOI10.1109/36.477187
- Bruzzone L., Serpico S. B., Feature selection in multiclass cases: a proposal and an experimental investigation, In: Proceedings of the 1st Workshop on Statistical Techniques in Pattern Recognition, Prague 1997, pp. 19–24 (1997)
- Fukunaga K., Introduction to Statistical Pattern Recognition, Second edition. Academic, New York 1990 Zbl0711.62052MR1075415
- Liu S. S., Jernigan M. E., 10.1016/0734-189X(90)90162-O, Computer Vision Image Processing 49 (1990), 52–67 (1990) DOI10.1016/0734-189X(90)90162-O
- Swain P. H., Davis S. M., Remote Sensing: The Quantitative Approach, McGraw–Hill, New York 1994
- Tou J. T., Gonzalez R. C., Pattern Recognition Principles, Addison–Wesley, London 1974 Zbl0299.68058MR0449069
NotesEmbed ?
topYou must be logged in to post comments.
To embed these notes on your page include the following JavaScript code on your page where you want the notes to appear.