Mining decision-rule preference model from rough approximation of preference relation

Given a ranking of actions evaluated by a set of evaluation criteria, we construct a rough approximation of the preference relation known from this ranking. The rough approximation of the preference relation is a starting point for mining " if... then" decision rules constituting a symbolic preference model. The set of rules is induced such as to be compatible with a concordance-discordance preference model used in well-known multicriteria decision aiding methods. An application of the set of decision rules to a new set of actions gives a fuzzy outranking graph. Positive and negative flows are calculated for each action in the graph, giving arguments about its strength and weakness. Aggregation of both arguments leads to a final ranking, either partial or complete. The approach can be applied to support a multicriteria choice and ranking of actions when the input information is a ranking of some reference actions.