2014

paper

english

EN We consider decision under uncertainty where preference information provided by a Decision Maker (DM) is a classification of some reference acts, relatively well-known to the DM, described by outcomes to be gained with given probabilities. We structure the classification data using a variant of the Dominance-based Rough Set Approach. Then, we induce from this data all possible minimal-cover sets of rules which correspond to all instances of the preference model compatible with the input preference information. We apply these instances on a set of unseen acts, and draw robust conclusions about their quality using the Robust Ordinal Regression paradigm. Specifically, for each act we derive the necessary and possible assignments specifying the range of classes to which the act is assigned by all or at least one compatible set of rules, respectively, as well as class acceptability indices. The whole approach is illustrated by a didactic example.

77 - 87

10.1007/978-3-319-08729-0_7

https://link.springer.com/chapter/10.1007/978-3-319-08729-0_7

Rough Sets and Intelligent Systems Paradigms : Second International Conference, RSEISP 2014, Granada and Madrid, Spain, July 9-13, 2014 : proceedings

2nd International Conference on Rough Sets and Emerging Intelligent Systems Paradigms (RSEISP) held as part of Joint Rough Set Symposium (JRS), 9-13.07.2014, Granada, Spain, Madrid, Spain

WoS (15)