Learning to Quantify: Methods and Applications (LQ 2021)

ABSTRACT

Learning to Quantify (LQ) is the task of training class prevalence estimators via supervised learning. The task of these estimators is to estimate, given an unlabelled set of data items D and a set of classes C = {c1, . . . , c|C|}, the prevalence (i.e., relative frequency) of each class ci in D. LQ is interesting in all applications of classification in which the final goal is not determining which class (or classes) individual unlabelled data items belong to, but estimating the distribution of the unlabelled data items across the classes of interest. Example disciplines whose interest in labelling data items is at the aggregate level (rather than at the individual level) are the social sciences, political science, market research, ecological modelling, and epidemiology. While LQ may in principle be solved by classifying each data item in D and counting how many such items have been labelled with ci, it has been shown that this “classify and count” (CC) method yields suboptimal quantification accuracy. As a result, quantification is now no longer considered a mere byproduct of classification and has evolved as a task of its own. The goal of this workshop is bringing together all researchers interested in methods, algorithms, and evaluation measures and methodologies for LQ, as well as practitioners interested in their practical application to managing large quantities of data.