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dc.contributor.authorCalders, Toon
dc.contributor.authorKarim, Asim A.
dc.contributor.authorKamiran, Faisal
dc.contributor.authorAli, Wasif Mohammad
dc.contributor.authorZhang, Xiangliang
dc.date.accessioned2015-08-04T07:17:26Z
dc.date.available2015-08-04T07:17:26Z
dc.date.issued2013-12
dc.identifier.citationCalders, T., Karim, A., Kamiran, F., Ali, W., & Zhang, X. (2013). Controlling Attribute Effect in Linear Regression. 2013 IEEE 13th International Conference on Data Mining. doi:10.1109/icdm.2013.114
dc.identifier.issn15504786
dc.identifier.doi10.1109/ICDM.2013.114
dc.identifier.urihttp://hdl.handle.net/10754/564826
dc.description.abstractIn 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.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectBatch Effects
dc.subjectFair Data Mining
dc.subjectLinear Regression
dc.subjectPropensity Score
dc.titleControlling attribute effect in linear regression
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.contributor.departmentMachine Intelligence & kNowledge Engineering Lab
dc.identifier.journal2013 IEEE 13th International Conference on Data Mining
dc.conference.date7 December 2013 through 10 December 2013
dc.conference.name13th IEEE International Conference on Data Mining, ICDM 2013
dc.conference.locationDallas, TX
dc.contributor.institutionComputer and Decision Engineering Dept., Universite Libre de Bruxelles (ULB), Belgium
dc.contributor.institutionDept. of Computer Science, SBASSE, Lahore University of Management Sciences (LUMS), Pakistan
kaust.personZhang, Xiangliang
kaust.personKamiran, Faisal


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