1. A computer implemented method of generating a classifier, comprising: obtaining a set of training data comprising labeled cases; training a classifier based, at least in part, on the training data; applying the classifier to a plurality of unlabeled cases to generate classification scores for each of the unlabeled cases, wherein each classification score corresponds with an instance of a corresponding case; and wherein the classification score corresponding to a first instance in a case is computed based, at least in part, on a value of a case-centric feature corresponding to the first instance, wherein the value of the case-centric feature is based, at least in part, on characteristics of the first instance and a second instance in the case.