Feature selection: Evaluation, application, and small sample performance
Feature selection: Evaluation, application, and small sample performance
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DOI：
10.1109/34.574797
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发表时间：
1997-02-01
影响因子：
23.6
通讯作者：
Zongker, D
中科院分区：
文献类型：
--
作者：
Jain, A;Zongker, D
目前已经提出了大量的特征子集选择算法。我们的实验结果表明，Pudil等人提出的顺序正向浮动选择（SFFS）算法在测试的其他算法中占主导地位。基于四种不同的纹理模型，研究了SAR卫星影像土地利用分类的最优特征集选择问题。将来自不同纹理模型的特征池化，然后进行特征选择，可以显著提高分类精度。我们还说明了在小样本量情况下使用特征选择的危险。
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.