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Erschienen in:
2010 | OriginalPaper | Buchkapitel
Dominance-Based Rough Set Approach to Preference Learning from Pairwise Comparisons in Case of Decision under Uncertainty
verfasst von : Salvatore Greco, Benedetto Matarazzo, Roman Słowiński
Erschienen in: Computational Intelligence for Knowledge-Based Systems Design
Verlag: Springer Berlin Heidelberg
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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
′.