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Training set size requirements for the classification of a specific class

Training set size requirements for the classification of a specific class
Training set size requirements for the classification of a specific class
The design of the training stage of a supervised classification should account for the properties of the classifier to be used. Consideration of the way the classifier operates may enable the training stage to be designed in a manner which ensures that the aim of the classification is satisfied with the use of a small, inexpensive, training set. It may, therefore, be possible to reduce the training set size requirements from that generally expected with the use of standard heuristics. Substantial reductions in training set size may be possible if interest is focused on a single class. This is illustrated for mapping cotton in north-western India by support vector machine type classifiers. Four approaches to reducing training set size were used: intelligent selection of the most informative training samples, selective class exclusion, acceptance of imprecise descriptions for spectrally distinct classes and the adoption of a one-class classifier. All four approaches were able to reduce the training set size required considerably below that suggested by conventional widely used heuristics without significant impact on the accuracy with which the class of interest was classified. For example, reductions in training set size of ? 90% from that suggested by a conventional heuristic are reported with the accuracy of cotton classification remaining nearly constant at ?95% and ?97% from the user's and producer's perspectives respectively
0034-4257
1-14
Foody, G.M.
06e50027-603d-4a5b-88f5-af2bb6235a37
Mathur, A.
d0f6d785-628a-4b85-89ba-f9afaf3011d8
Sanchez-Hernandez, C.
3f2cb6be-152e-4579-8b7a-8e3b276cd021
Boyd, D.S.
cc3e74df-9587-4328-a591-f67144fffa82
Foody, G.M.
06e50027-603d-4a5b-88f5-af2bb6235a37
Mathur, A.
d0f6d785-628a-4b85-89ba-f9afaf3011d8
Sanchez-Hernandez, C.
3f2cb6be-152e-4579-8b7a-8e3b276cd021
Boyd, D.S.
cc3e74df-9587-4328-a591-f67144fffa82

Foody, G.M., Mathur, A., Sanchez-Hernandez, C. and Boyd, D.S. (2006) Training set size requirements for the classification of a specific class. Remote Sensing of Environment, 104 (1), 1-14. (doi:10.1016/j.rse.2006.03.004).

Record type: Article

Abstract

The design of the training stage of a supervised classification should account for the properties of the classifier to be used. Consideration of the way the classifier operates may enable the training stage to be designed in a manner which ensures that the aim of the classification is satisfied with the use of a small, inexpensive, training set. It may, therefore, be possible to reduce the training set size requirements from that generally expected with the use of standard heuristics. Substantial reductions in training set size may be possible if interest is focused on a single class. This is illustrated for mapping cotton in north-western India by support vector machine type classifiers. Four approaches to reducing training set size were used: intelligent selection of the most informative training samples, selective class exclusion, acceptance of imprecise descriptions for spectrally distinct classes and the adoption of a one-class classifier. All four approaches were able to reduce the training set size required considerably below that suggested by conventional widely used heuristics without significant impact on the accuracy with which the class of interest was classified. For example, reductions in training set size of ? 90% from that suggested by a conventional heuristic are reported with the accuracy of cotton classification remaining nearly constant at ?95% and ?97% from the user's and producer's perspectives respectively

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More information

Submitted date: 15 December 2005
Published date: 2006

Identifiers

Local EPrints ID: 57706
URI: http://eprints.soton.ac.uk/id/eprint/57706
ISSN: 0034-4257
PURE UUID: 9ba7c059-ac2f-47ec-96bc-d2ccc3c65746

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Date deposited: 11 Aug 2008
Last modified: 15 Mar 2024 11:08

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Contributors

Author: G.M. Foody
Author: A. Mathur
Author: C. Sanchez-Hernandez
Author: D.S. Boyd

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