Feature selection: Evaluation, application, and small sample performance

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Feature selection: Evaluation, application, and small sample performance (1997)

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by Anil Jain , Douglas Zongker

Venue:	IEEE Transactions on Pattern Analysis and Machine Intelligence
Citations:	471 - 13 self

BibTeX

@ARTICLE{Jain97featureselection:,
    author = {Anil Jain and Douglas Zongker},
    title = {Feature selection: Evaluation, application, and small sample performance},
    journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
    year = {1997},
    volume = {19},
    pages = {153--158}
}

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Abstract

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. Index Terms—Feature selection, curse of dimensionality, genetic algorithm, node pruning, texture models, SAR image classification. 1

Keyphrases

feature selection small sample performance different texture model index term feature selection sar image classification genetic algorithm sequential forward feature selection result optimal feature land use classification texture model classification accuracy large number node pruning small sample size situation sar satellite image experimental result substantial improvement

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