  Features selection is an essential step for successful data classification,
since it reduces the data dimensionality by removing redundant features.
Consequently, that minimizes the classification complexity and time in addition
to maximizing its accuracy. In this article, a comparative study considering
six features selection heuristics is conducted in order to select the best
relevant features subset. The tested features vector consists of fourteen
features that are computed for each pixel in the field of view of retinal
images in the DRIVE database. The comparison is assessed in terms of
sensitivity, specificity, and accuracy measurements of the recommended features
subset resulted by each heuristic when applied with the ant colony system.
Experimental results indicated that the features subset recommended by the
relief heuristic outperformed the subsets recommended by the other experienced
heuristics.
