Class distribution estimation (quantification) plays an important role in many practical
classification problems. Firstly, it is important in order to adapt the classifier to the …

We address the problem of quantification, a supervised learning task whose goal is, given a
class, to estimate the relative frequency (or prevalence) of the class in a dataset of unlabeled …

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 …

Several disciplines, like the social sciences, epidemiology, sentiment analysis, or market
research, are interested in knowing the distribution of the classes in a population rather than …

In many applications there is a need to monitor how a population is distributed across
different classes, and to track the changes in this distribution that derive from varying …

A quantification learning task estimates class ratios or class distribution given a test set.
Quantification learning is useful for a variety of application domains such as commerce …

Decision support systems for surveillance rely more and more on face recognition (FR) to
detect target individuals of interest captured with video cameras. FR is a challenging …

Despite the existence of patterns able to discriminate between consensual and non-
consensual intercourse, the relevance of genital lesions in the corroboration of a legal rape …

Several ensemble-based techniques have been proposed to design pattern recognition
systems when data has imbalanced class distributions, although class proportions may …