The task of quantification consists in providing an aggregate estimation (eg, the class
distribution in a classification problem) for unseen test sets, applying a model that is trained …

The increasing capacities of large language models (LLMs) have been shown to present an
unprecedented opportunity to scale up data analytics in the humanities and social sciences …

The study of marine plankton data is vital to monitor the health of the world's oceans. In
recent decades, automatic plankton recognition systems have proved useful to address the …

Prior probability shift is a phenomenon where the training and test datasets differ structurally
within population subgroups. This phenomenon can be observed in the yearly records of …

This open access book provides an introduction and an overview of learning to quantify (aka
“quantification”), ie the task of training estimators of class proportions in unlabeled data by …

Quantification is the task of estimating, given a set σ σ of unlabelled items and a set of
classes C={c_ 1, ..., c_| C|\} C= c 1,…, c| C|, the prevalence (or “relative frequency”) in σ σ of …

Ensembles are among the most effective and successful methods for almost all supervised
tasks. Not long ago, an ensemble approach has been proposed for quantification learning …

Quantification (or prevalence estimation) algorithms aim at predicting the class distribution of
unseen sets (or bags) of examples. These methods are useful for two main tasks: 1) …

Quantification is an expanding research topic in Machine Learning literature. While in
classification we are interested in obtaining the class of individual observations, in …

Learning to quantify (aka quantification) is a task concerned with training unbiased
estimators of class prevalence via supervised learning. This task originated with the …