  We address the problem of \emph{quantification}, a supervised learning task
whose goal is, given a class, to estimate the relative frequency (or
\emph{prevalence}) of the class in a dataset of unlabelled items.
Quantification has several applications in data and text mining, such as
estimating the prevalence of positive reviews in a set of reviews of a given
product, or estimating the prevalence of a given support issue in a dataset of
transcripts of phone calls to tech support. So far, quantification has been
addressed by learning a general-purpose classifier, counting the unlabelled
items which have been assigned the class, and tuning the obtained counts
according to some heuristics. In this paper we depart from the tradition of
using general-purpose classifiers, and use instead a supervised learning model
for \emph{structured prediction}, capable of generating classifiers directly
optimized for the (multivariate and non-linear) function used for evaluating
quantification accuracy. The experiments that we have run on 5500 binary
high-dimensional datasets (averaging more than 14,000 documents each) show that
this method is more accurate, more stable, and more efficient than existing,
state-of-the-art quantification methods.
