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Dominance-Based Rough Set Approach to Preference Learning from Pairwise Comparisons in Case of Decision under Uncertainty

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 6178)

Abstract

We deal with preference learning from pairwise comparisons, in case of decision under uncertainty, using a new rough set model based on stochastic dominance applied to a pairwise comparison table. For the sake of simplicity we consider the case of traditional additive probability distribution over the set of states of the world; however, the model is rich enough to handle non-additive probability distributions, and even qualitative ordinal distributions. The rough set approach leads to a representation of decision maker’s preferences under uncertainty in terms of “if..., then...” decision rules induced from rough approximations of sets of exemplary decisions. An example of such decision rule is “if act a is at least strongly preferred to act a′ with probability at least 30%, and a is at least weakly preferred to act a′ with probability at least 60%, then act a is at least as good as act a′.

Keywords

  • Decision under uncertainty
  • Dominance-based Rough Set Approach
  • Pairwise Comparison Table
  • Decision rules
  • Preference learning

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Greco, S., Matarazzo, B., Słowiński, R. (2010). Dominance-Based Rough Set Approach to Preference Learning from Pairwise Comparisons in Case of Decision under Uncertainty. In: Hüllermeier, E., Kruse, R., Hoffmann, F. (eds) Computational Intelligence for Knowledge-Based Systems Design. IPMU 2010. Lecture Notes in Computer Science(), vol 6178. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14049-5_60

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  • DOI: https://doi.org/10.1007/978-3-642-14049-5_60

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14048-8

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