Methods for Learning to Quantify

AbstractThis chapter is possibly the central chapter of the book, and looks at the various supervised learning methods for learning to quantify that have been proposed over the years. These methods belong to two main categories, depending on whether they have an aggregative nature (i.e., they require the classification of all individual unlabelled items as an intermediate step) or a non-aggregative nature (i.e., they perform no classification of individual items). In turn, the aggregative methods may be seen as belonging to two main sub-categories, depending on whether the classification of individual unlabelled items is performed by classifiers trained via general-purpose learners or via special-purpose, quantification-oriented learners.