A model of user-oriented reduct construction for machine learning
Transactions on rough sets VIII
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
Decision rule-based data models using TRS and NetTRS – methods and algorithms
Transactions on Rough Sets XI
On variable consistency dominance-based rough set approaches
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
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Given a ranking of actions evaluated by a set of evaluation criteria, we are constructing 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驴, then驴" 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. 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 appliedto support multicriteria choice and ranking of actions when the input information is a ranking ofsome reference actions.