Since its conception, the dominance-based rough set approach (DRSA) has been adapted to a large variety of decision problems. In this chapter we outline the rough set methodology designed for multi-attribute decision aiding. DRSA was proposed as an extension of the Pawlak concept of rough sets in order to deal with ordinal data. We focus on decision problems where all attributes describing objects of a decision problem have ordered value sets (scales). Such attributes are called criteria, and thus the problems are called multi-criteria decision problems. Criteria are real-valued functions of gain or cost type, depending on whether a greater value is better or worse, respectively. In these problems, we also assume the presence of a well defined decision maker (DM) (single of group DM) concerned by multi-criteria classification, choice, and ranking.

Rough set methodology for decision aiding

Greco, Salvatore;Matarazzo, Benedetto
2015

Abstract

Since its conception, the dominance-based rough set approach (DRSA) has been adapted to a large variety of decision problems. In this chapter we outline the rough set methodology designed for multi-attribute decision aiding. DRSA was proposed as an extension of the Pawlak concept of rough sets in order to deal with ordinal data. We focus on decision problems where all attributes describing objects of a decision problem have ordered value sets (scales). Such attributes are called criteria, and thus the problems are called multi-criteria decision problems. Criteria are real-valued functions of gain or cost type, depending on whether a greater value is better or worse, respectively. In these problems, we also assume the presence of a well defined decision maker (DM) (single of group DM) concerned by multi-criteria classification, choice, and ranking.
9783662435052
Computer Science (all)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11769/361614
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