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Dataset Shift in Machine Learning$
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Joaquin Quiñonero-Candela, Masashi Sugiyama, Anton Schwaighofer, and Neil D. Lawrence

Print publication date: 2008

Print ISBN-13: 9780262170055

Published to MIT Press Scholarship Online: August 2013

DOI: 10.7551/mitpress/9780262170055.001.0001

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When Training and Test Sets Are Different: Characterizing Learning Transfer

When Training and Test Sets Are Different: Characterizing Learning Transfer

Chapter:
(p.2) (p.3) 1 When Training and Test Sets Are Different: Characterizing Learning Transfer
Source:
Dataset Shift in Machine Learning
Author(s):

Storkey Amos

Publisher:
The MIT Press
DOI:10.7551/mitpress/9780262170055.003.0001

This chapter introduces the general learning transfer problem and formulates it in terms of a change of scenario. Standard regression and classification models can be characterized as conditional models. Assuming that the conditional model is true, covariate shift is not an issue. However, if this assumption does not hold, conditional modeling will fail. The chapter then characterizes a number of different cases of dataset shift, including simple covariate shift, prior probability shift, sample selection bias, imbalanced data, domain shift, and source component shift. Each of these situations is cast within the framework of graphical models and a number of approaches to addressing each of these problems are reviewed. The chapter also presents a framework for multiple dataset learning that prompts the possibility of using hierarchical dataset linkage.

Keywords:   general learning transfer, conditional model, covariate shift, dataset shift, dataset learning, hierarchical dataset linkage

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