1. A method of selecting features for classification that trades classification efficiency for computational resources, the method comprising: ranking a plurality of features of a training set according to how closely they are correlated to their corresponding classifications; extracting a plurality of features from data; and selecting a subset of the features of the data, according to the ranking of the features of the training data such that a computational resource cost of the subset is less than a predefined computational resource maximum and the degree of utility achieved by a classification of the subset of features by a selected classifier is optimized and exceeds a predefined utility minimum.