Publication
Title
Controlling attribute effect in linear regression
Author
Abstract
In data mining we often have to learn from biased data, because, for instance, data comes from different batches or there was a gender or racial bias in the collection of social data. In some applications it may be necessary to explicitly control this bias in the models we learn from the data. This paper is the first to study learning linear regression models under constraints that control the biasing effect of a given attribute such as gender or batch number. We show how propensity modeling can be used for factoring out the part of the bias that can be justified by externally provided explanatory attributes. Then we analytically derive linear models that minimize squared error while controlling the bias by imposing constraints on the mean outcome or residuals of the models. Experiments with discrimination-aware crime prediction and batch effect normalization tasks show that the proposed techniques are successful in controlling attribute effects in linear regression models.
Language
English
Source (book)
IEEE 13th International Conference on Data Mining (ICDM), December 07-10, 2013, Dallas, Texas
Publication
New York, N.Y. : IEEE, 2013
Volume/pages
(2013), p. 71-80
ISI
000332874200008
Full text (Publisher's DOI)
UAntwerpen
Faculty/Department
Publication type
Subject
External links
Web of Science
Record
Identification
Creation 23.06.2016
Last edited 09.09.2017