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Mathematical Programming for Missing Data AbstractWe propose a mathematical programming method to deal with uncertainty in the observations of a classification problem. This means that we can deal with situations where instead of a sample $(\xb_i, y_i)$ we may only have a distribution over $(\xb_i, y_i)$ at our disposition. In particular, we derive a robust formulation when the uncertainty is given by a normal distribution. This leads to Second Order Cone Programming Problems. Our method can be applied to the problem of missing data, where it outperforms direct imputation.
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