Quantification represents the problem of predicting class distributions in a dataset. It also
represents a growing research field in supervised machine learning, for which a large …

Quantification is a thriving research area that develops methods to estimate the class prior
probabilities in an unlabelled set of observations. Quantification and classification share …

Quantification research proposes methods to estimate the class distribution in an
independent sample. Many areas, such as epidemiology, sentiment analysis, political …

For the binary prevalence quantification problem under prior probability shift, we determine
the asymptotic variance of the maximum likelihood estimator. We find that it is a function of …

Quantification is a research area that develops methods that estimate the class attribute
prevalence in an independent sample. Like the other fields in Machine Learning …

When applying supervised learning to estimate class distributions of unlabelled samples (so-
called quantification), dataset shift is an expected yet challenging problem. Existing …

Abstract The International Classification of Diseases (ICD) has been adopted worldwide in
the healthcare domain, eg to summarize the key information in clinical documents. Since …

Quantification research proposes methods to estimate the class distribution in an
independent sample. Quantification methods find applications in areas that rely on …

Quantification research has sought to accurately estimate class distributions under dataset
shift. While existing methods perform well under assumed conditions of shift, it is not always …

This paper evaluates sentiment quantification methods applied to Brazilian Portuguese
corpora. Sentiment quantification, distinct from sentiment classification, estimates the …