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Optimization problems due to 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.In this paper, we introduce a framework that considers the uncertain data implicitly. We define the concept of Uncertainty Features (UF), which are problem-specific structural properties of a solution. We show how to formulate an uncertain problem using the Uncertainty Feature Optimization (UFO) framework as a multi-objective problem. We show that stochastic programming and robust optimization are particular cases of the UFO framework. We present computational results for the Multi-Dimensional Knapsack Problem (MDKP) and discuss the application of the framework to the airline scheduling problem.

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