Ensemble learning techniques have achieved state-of-the-art performance in diverse
machine learning applications by combining the predictions from two or more base models …

Credit risk assessment is usually regarded as an imbalanced classification task solved by
static ensemble classifiers. However, the dynamic ensemble selection (DES) strategy that …

Many machine learning and data mining tasks are based on distance measures, so a large
amount of literature addresses this aspect somehow. Due to the broad scope of the topic …

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 …

QuaPy is an open-source framework for performing quantification (aka supervised
prevalence estimation), written in Python. Quantification is the task of training quantifiers via …

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 the era of web 2.0, online forums, blogs and Twitter are becoming primary sources for
sharing views, opinions and comments about different topics. Classifying these views …

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

Sentiment quantification is the task of training, by means of supervised learning, estimators
of the relative frequency (also called “prevalence”) of sentiment-related classes (such as …