Feature selection: evaluation, application, and small sample performance

TLDR

A large number of algorithms have been proposed for feature subset selection. The study aims to identify an optimal feature set for land use classification from SAR satellite images. This is achieved by employing four distinct texture models. Sequential forward floating selection outperforms other methods, and combining features from multiple texture models with selection substantially improves land‑use classification accuracy, though the approach is risky with small sample sizes.

Abstract

A large number of algorithms have been proposed for feature subset selection. Our experimental results show that the sequential forward floating selection algorithm, proposed by Pudil et al. (1994), 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.