Explicit Loss Minimization in Quantification Applications (Preliminary Draft)

In recent years there has been a growing interest in quantification, a variant of classification in which the final goal is not accurately classifying each unlabelled document but accurately estimating the prevalence (or “relative frequency”) of each class c in the unlabelled set. Quantification has several applications in information retrieval, data mining, machine learning, and natural language processing, and is a dominant concern in fields such as market research, epidemiology, and the social sciences. This paper describes recent research in addressing quantification via explicit loss minimization, discussing works that have adopted this approach and some open questions that they raise.