1. A method comprising: training a Support Vector Machine (SVM) on a subset of features (d′) of a feature set having (d) features of a plurality of training instances to obtain a weight per instance ({right arrow over (α)}′); approximating a quality for the d features of the feature set using the weight per instance; ranking the d features of the feature set based on the approximated quality; and selecting a subset (q) of the features of the feature set based on the ranked approximated weights.