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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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PRINTED FROM MIT PRESS SCHOLARSHIP ONLINE (www.mitpress.universitypressscholarship.com). (c) Copyright The MIT Press, 2020. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in MITSO for personal use.date: 09 April 2020

Binary Classification under Sample Selection Bias

Binary Classification under Sample Selection Bias

Chapter:
(p.40) (p.41) 3 Binary Classification under Sample Selection Bias
Source:
Dataset Shift in Machine Learning
Author(s):

Hein Matthias

Publisher:
The MIT Press
DOI:10.7551/mitpress/9780262170055.003.0003

This chapter examines the problem of binary classification under sample selection bias from a decision-theoretic perspective. Starting from a derivation of the necessary and sufficient conditions for equivalence of the Bayes classifiers of training and test distributions, it provides the conditions under which sample selection bias does not affect the performance of a classifier. From this viewpoint, there are fundamental differences between classifiers of low and high capacity, in particular the ones that are Bayes consistent. The second part of the chapter provides means to modify existing learning algorithms such that they are more robust to sample selection bias in the case where one has access to an unlabeled sample of the test data. This is achieved by constructing a graph-based regularization functional. The close connection of this approach to semisupervised learning is also highlighted.

Keywords:   Bayes classifiers, sample selection bias, learning algorithms, regularization functional

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