Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In data mining  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  learn from the data.  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.  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  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.
['Toon Calders', 'Asim Karim', 'Faisal Kamiran', 'Wasif M. Ali', 'Xiangliang Zhang']
I am writing a research paper on a idea Controlling Attribute Effect in Linear Regression
[{"from": "human", "value": "I am writing a research paper on a idea Controlling Attribute Effect in Linear Regression"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In data mining  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  learn from the data.  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.  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  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."}]