ELECCALC—an interactive software for modelling the decision maker's preferences
Proceedings of the conference on First specialized conference on decision support systems
Inferring an ELECTRE TRI Model from Assignment Examples
Journal of Global Optimization
Rough Set Learning of Preferential Attitude in Multi-Criteria Decision Making
ISMIS '93 Proceedings of the 7th International Symposium on Methodologies for Intelligent Systems
An Algorithm for Induction of Decision Rules Consistent with the Dominance Principle
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Data mining tasks and methods: Classification: multicriteria classification
Handbook of data mining and knowledge discovery
Decision support for risk analysis on dynamic alliance
Decision Support Systems
Dominance-Based Rough Set Approach to Interactive Multiobjective Optimization
Multiobjective Optimization
Spatial decision support for assisted housing mobility counseling
Decision Support Systems
A multicriteria decision support system for bank rating
Decision Support Systems
Sequential covering rule induction algorithm for variable consistency rough set approaches
Information Sciences: an International Journal
A GIS-based multicriteria spatial decision support system for planning urban infrastructures
Decision Support Systems
Hi-index | 0.00 |
We propose to apply the Dominance-based Rough Set Approach (DRSA) on the results of multiple criteria decision aiding (MCDA) methods, in order to explain their recommendations in terms of rules involving conditions on evaluation criteria. The rules represent a decision model which is transparent and easy to interpret for the DM. In fact, decision rules give arguments to justify and explain the decision and, in a learning perspective, they can be the starting point for an interactive procedure for analyzing and constructing the DM's preferences. It enables his/her understanding of the conditions for the suggested recommendation, and provides useful information about the role of particular criteria or their subsets. DRSA can be used in junction with any MCDA method producing a classification result or a preference relation in the set of alternatives. In this paper, we apply DRSA to a recently proposed MCDA methodology, called Robust Ordinal Regression (ROR). The ROR approach to MCDA, also called disaggregation-aggregation approach, aims at inferring parameters of a preference model representing some holistic preference comparisons of alternatives provided by the decision maker (DM). Contrary to the usual ordinal regression approaches to MCDA, ROR takes into account the whole set of possible value of preference model parameters compatible with the DM's preference information, to work out a final recommendation. In consequence, ROR gives a recommendation in terms of necessary and possible consequences of the application of all the compatible sets of parameter values to the considered set of alternatives. UTA^G^M^S and GRIP methods apply this approach, considering general monotonic additive value functions, and produce as a result the necessary and possible preference relations. In this paper we show how DRSA completes the decision aiding process started with ROR, providing a very useful interpretation of the preference relations in terms of decision rules.