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Dominance-based rough set approach to multiple criteria decision support

Roman Słowiński, Salvatore Greco, Benedetto Matarazzo


The utility of the rough set approach to multiple criteria decision support is related to the nature of both, the input preferential information available in decision analysis, and the output of the analysis. As to the input, the rough set approach requires a set of decision examples. This is convenient for the acquisition of preferential information from decision makers. Very often in multiple criteria decision support, this information has to be given in terms of preference model parameters, such as importance weights, substitution ratios and various thresholds. Producing such information requires a significant cognitive effort on the part of the decision maker. It is generally acknowledged that people often prefer to make exemplary decisions and cannot always explain them in terms of specific parameters. For this reason, the idea of inferring preference models from exemplary decisions provided by the decision maker is very attractive. Furthermore, the exemplary decisions may be inconsistent because of limited clear discrimination between values of particular criteria and because of hesitation on the part of the decision maker. These inconsistencies can convey important information that should be taken into account in the construction of the decision maker?s preference model. The rough set approach is intended to deal with inconsistency and this is a major argument to support its application to multiple criteria decision analysis. The output of the analysis, i.e. the model of preferences in terms of "if..., then..." decision rules, is very convenient for decision support because it is intelligible and speaks the same language as the decision maker. The rough set approach adapted to multiple criteria decision support is called Dominance-based Rough Set Approach (DRSA). DRSA is concordant with the concept of granular computing, however, the granules are dominance cones in evaluation space and not bounded sets as it is the case in the basic rough set approach. It is also concordant with the paradigm of computing with words, as it exploits ordinal, and not necessarily cardinal, character of data. We present DRSA for multiple criteria classification, choice and ranking, as well as DRSA for decisions under risk. Finally, we compare DRSA with other decision support paradigms at an axiomatic level.


Rough sets, multiple criteria decision support, decision under risk, knowledge discovery, preference model, decision rules

Reference index:

Roman Słowiński, Salvatore Greco, Benedetto Matarazzo, (2007), Dominance-based rough set approach to multiple criteria decision support, Multiple Criteria Decision Making (2), pp. 9-56

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Scopus citations in 1 paper(s):
  1. Raza, M. S., & Qamar, U. (2019). A parallel approach to calculate lower and upper approximations in dominance based rough set theory. Applied Soft Computing Journal, 84 doi:10.1016/j.asoc.2019.105699