Optimization problems with noisy data solved using stochastic programming or robust optimization approaches require the explicit characterization of an uncertainty set U that models the nature of the noise. Such approaches depend on the modeling of the uncertainty set and suffer from an erroneous estimation of the noise.
@Article{EggeSalaBier10,
author = {Niklaus Eggenberg and Matteo Salani and Michel Bierlaire},
title = {Uncertainty Feature Optimization: an implicit paradigm for problems with noisy data},
journal = {Networks},
year = {2011},
volume = {57},
number = {3},
pages = {270-284},
DOI = {10.1002/net.20428},
note = {Accepted on Jun 16, 2010}}}