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 additionto maximizing its accuracy. In this article, a comparative study consideringsix features selection heuristics is conducted in order to select the bestrelevant features subset. The tested features vector consists of fourteenfeatures that are computed for each pixel in the field of view of retinalimages in the DRIVE database. The comparison is assessed in terms ofsensitivity, specificity, and accuracy measurements of the recommended featuressubset resulted by each heuristic when applied with the ant colony system.Experimental results indicated that the features subset recommended by therelief heuristic outperformed the subsets recommended by the other experiencedheuristics.
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