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A Review on Quantification Learning
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DOI：10.1145/3117807.png)
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The task of quantification consists in providing an aggregate estimation (e.g., the class distribution in a classification problem) for unseen test sets, applying a model that is trained using a training set with a different data distribution. Several real-world applications demand this kind of method that does not require predictions for individual examples and just focuses on obtaining accurate estimates at an aggregate level. During the past few years, several quantification methods have been proposed from different perspectives and with different goals. This article presents a unified review of the main approaches with the aim of serving as an introductory tutorial for newcomers in the field.
Keyword:
Class distribution estimation
prevalence estimation
quantification
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Semi-supervised learning of class balance under class-prior change by distribution matching.png)
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