Abstract
The preference disaggregation framework of multicriteria decision aid focuses on inferring decision models from data. In this context, the robustness of the results is of major importance to ensure that quality recommendations are provided. In this chapter we examine this issue adopting a data-driven perspective, focusing on the effect due to changes in the data used for model construction. The analysis is implemented for decision models expressed in the form of additive value functions for multicriteria classification problems. Simple analytic measures are introduced based on well-known optimization tools. The proposed measures enrich existing robust multicriteria approaches with additional information taken directly from the available data though an analytical approach. The properties and performance of the new robustness indicators are illustrated through their application to an example data set.
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Doumpos, M., Zopounidis, C. (2016). Data-Driven Robustness Analysis for Multicriteria Classification Problems Using Preference Disaggregation Approaches. In: Doumpos, M., Zopounidis, C., Grigoroudis, E. (eds) Robustness Analysis in Decision Aiding, Optimization, and Analytics. International Series in Operations Research & Management Science, vol 241. Springer, Cham. https://doi.org/10.1007/978-3-319-33121-8_2
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