1. A method of automated machine-learning classification, comprising: establishing, within a computer, an original feature set, each feature of the original feature set having a predictive value, the predictive value of some features being uncertain for characterizing expected input items during classification thereof; selecting with the computer a feature set, the feature set being a subset of the original feature set; obtaining to the computer a number of training items having values for a plurality of different features in the feature set; calculating with the computer scores for the different features of the feature set using a scoring technique, the score for a given feature being a measure of prediction ability for the given feature and calculated as S=|aF −1 (tpr)−bF −1 (fpr)|, where S is the score, tpr is the true positive rate of the given feature equal to a number of positive training cases containing a subject feature divided by a number of positive training cases, fpr is the false positive rate of the given feature equal to a number of negative training cases containing the subject feature divided by a number of negative training cases, |*| is an absolute value, F − (*) is an inverse of an assumed probability distribution function, and a and b are constants; scaling the values for the features of the feature set with the computer according to the scores for said features as adjusted feature values; generating a classifier with the computer; training the classifier using the adjusted feature values for the features of the feature set; scaling the values for the features in the feature set of an input item with the computer according to the scores as adjusted feature values of the input item; and classifying an input item using the computer and the adjusted feature values for the input item into the previously trained classifier.