1. A computer-implemented method for generating a monotonic classifier, the method comprising: mapping a plurality of feature vectors to a plurality of ordered classes, wherein each feature vector maps to a corresponding class; for each class other than a lowest-ordered class, training, via a processor, a binary classifier to distinguish between the class and a preceding class, wherein training a binary classifier comprises: determining a data set impurity value for a data set of feature vectors, wherein the data set impurity value equals the sum, over all classes, of the product of (i) the number of feature vectors in the data set having a particular class divided by the number of feature vectors in the data set and (ii) the logarithm of the number of feature vectors in the data set having the particular class divided by the number of feature vectors in the data set, determining a decision rule for separating the data set into a plurality of subsets that maximizes the decrease in impurity, wherein the decrease in impurity equals the data set impurity value for the data set minus, for each subset, the product of a number of feature vectors in the subset and a data set impurity value for the subset divided by the number of feature vectors in the data set, and repeating the determining a data set impurity and the determining a decision rule on one or more subsets until the data set impurity value for each subset is minimized; and ordering, via the processor, the binary classifiers as a monotonic classifier based on the class corresponding to each binary classifier, wherein a number of classes in the plurality of ordered classes is one more than a number of binary classifiers.