We develop a general approach for solving constrained classification problems, where the
loss and constraints are defined in terms of a general function of the confusion matrix. We …

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 …

In late 2019, a new Coronavirus disease (COVID-19) appeared in Wuhan, Hubei Province,
China. The virus began to spread throughout many countries, affecting a large population …

In many scientific and engineering applications, we are tasked with the maximisation of an
expensive to evaluate black box function f. Traditional settings for this problem assume just …

Recognizing human activities using automated methods has emerged recently as a pivotal
research theme for security-related applications. In this research paper, an optical flow …

Classifying e-mails into distinct labels can have a great impact on customer support. By
using machine learning to label e-mails, the system can set up queues containing e-mails of …

Sentiment Quantification aims to detect the overall sentiment polarity of users from a set of
reviews corresponding to a target. Existing methods equally treat and aggregate individual …

Optimization of reactive power dispatch (ORPD) problem is a key factor for stable and
secure operation of the electric power systems. In this paper, a newly explored nature …

This paper presents the application of an improved stochastic fractal search algorithm
(ISFSA) for optimizing five single objectives of optimal power flow (OPF) problem and …