Joint European Conference on Machine Learning and Knowledge Discovery in Databases

ECML PKDD 2015: Machine Learning and Knowledge Discovery in Databases pp 71-86

A Practical Approach to Reduce the Learning Bias Under Covariate Shift

Conference paper

DOI: 10.1007/978-3-319-23525-7_5

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9285)
Cite this paper as:
Tran VT., Aussem A. (2015) A Practical Approach to Reduce the Learning Bias Under Covariate Shift. In: Appice A., Rodrigues P., Santos Costa V., Gama J., Jorge A., Soares C. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2015. Lecture Notes in Computer Science, vol 9285. Springer, Cham

Abstract

Covariate shift is a specific class of selection bias that arises when the marginal distributions of the input features X are different in the source and the target domains while the conditional distributions of the target Y given X are the same. A common technique to deal with this problem, called importance weighting, amounts to reweighting the training instances in order to make them resemble the test distribution. However this usually comes at the expense of a reduction of the effective sample size. In this paper, we show analytically that, while the unweighted model is globally more biased than the weighted one, it may locally be less biased on low importance instances. In view of this result, we then discuss a manner to optimally combine the weighted and the unweighted models in order to improve the predictive performance in the target domain. We conduct a series of experiments on synthetic and real-world data to demonstrate the efficiency of this approach.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.LIRIS, UMR 5205, University of Lyon 1LyonFrance

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