What can we expect from high-dimensional feature selection

High-throughput technologies for rapid measurement of vast numbers of biological variables like cDNA microarray technology offer the potential for highly discriminatory diagnosis and prognosis; however, high dimensionality together with small samples creates the need for feature selection, while at the same time making feature-selection algorithms less reliable. Through a regression approach, we found that (1) it is unlikely that feature selection will yield a feature set whose error is close to that of the optimal feature set; and (2) the inability to find a good feature set should not lead to the conclusion that good feature sets do not exist.