Journal
ACM COMPUTING SURVEYS
Volume 50, Issue 5, Pages -Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3117807
Keywords
Class distribution estimation; prevalence estimation; quantification
Categories
Funding
- Fulbright Commission
- Salvador de Madariaga Program [PRX15/00607]
- MINECO
- FEDER [TIN2015-65069-C2-2-R]
- NSF Grant [IIS-1447795]
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [1447795] Funding Source: National Science Foundation
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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.
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