Training set size requirements for the classification of a specific class
Saved in:
| Title: | Training set size requirements for the classification of a specific class |
|---|---|
| Authors: | Foody, Giles M.1 g.m.foody@soton.ac.uk, Mathur, Ajay2, Sanchez-Hernandez, Carolina3, Boyd, Doreen S4 |
| Source: | Remote Sensing of Environment. Sep2006, Vol. 104 Issue 1, p1-14. 14p. |
| Subjects: | Classification, Operations research, Cotton, Plant fibers |
| Abstract: | 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. [Copyright &y& Elsevier] |
| Copyright of Remote Sensing of Environment is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
Be the first to leave a comment!
