Feature Selection: Evaluation, Application, and Small Sample Performance

Authors:
Anil Jain;Douglas Zongker
Affiliations:
-;-
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
1997

Citing 1
Cited 259

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Abstract

A large number of algorithms have been proposed for feature subset selection. Our experimental results show that the sequential forward floating selection (SFFS) algorithm, proposed by Pudil et al., dominates the other algorithms tested. We study the problem of choosing an optimal feature set for land use classification based on SAR satellite images using four different texture models. Pooling features derived from different texture models, followed by a feature selection results in a substantial improvement in the classification accuracy. We also illustrate the dangers of using feature selection in small sample size situations.