The University of Southampton
University of Southampton Institutional Repository

Training set size requirements for the classification of a specific class

Record type: Article

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

Full text not available from this repository.

Citation

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), pp. 1-14. (doi:10.1016/j.rse.2006.03.004).

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

Catalogue record

Date deposited: 11 Aug 2008
Last modified: 17 Jul 2017 14:28

Export record

Altmetrics

Contributors

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

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×