@misc{10481/85892,
year = {2023},
month = {6},
url = {https://hdl.handle.net/10481/85892},
abstract = {Quantification, variously called supervised prevalence estimation or learning to quantify, is the supervised
learning task of generating predictors of the relative frequencies (a.k.a. prevalence values) of the classes of
interest in unlabelled data samples. While many quantification methods have been proposed in the past
for binary problems and, to a lesser extent, single-label multiclass problems, the multi-label setting (i.e.,
the scenario in which the classes of interest are not mutually exclusive) remains by and large unexplored.
A straightforward solution to the multi-label quantification problem could simply consist of recasting the
problem as a set of independent binary quantification problems. Such a solution is simple but naïve, since
the independence assumption upon which it rests is, in most cases, not satisfied. In these cases, knowing
the relative frequency of one class could be of help in determining the prevalence of other related classes.
We propose the first truly multi-label quantification methods, i.e., methods for inferring estimators of class
prevalence values that strive to leverage the stochastic dependencies among the classes of interest in order
to predict their relative frequencies more accurately. We show empirical evidence that natively multi-label
solutions outperform the naïve approaches by a large margin. The code to reproduce all our experiments is
available online.},
organization = {SoBigData++ project, funded by the European Commission (Grant 871042) under the H2020 Programme INFRAIA-2019-1},
organization = {AI4Media project, funded by the European Commission (Grant 951911) under the H2020 Programme ICT-48-2020},
organization = {SoBigData.it and FAIR projects funded by the Italian Ministry of University and Research under the NextGenerationEU program},
organization = {PI 2017 predoctoral programme, from the Spanish Ministry of Economy and Competitiveness (MINECO), grant BES-2017-081202},
publisher = {Association for Computing Machinery},
title = {Multi-Label Quantification},
doi = {10.1145/3606264},
author = {Moreo Fernández, Alejandro and Aparicio, Manuel Francisco and Sebastiani, Fabrizio},
}