Controlling attribute effect in linear regression

Toon Calders, Asim A. Karim, Faisal Kamiran, Wasif Mohammad Ali, Xiangliang Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

39 Scopus citations

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. © 2013 IEEE.
Original languageEnglish (US)
Title of host publication2013 IEEE 13th International Conference on Data Mining
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages71-80
Number of pages10
ISBN (Print)9780769551081
DOIs
StatePublished - Dec 2013

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