Rough Set Approach to Knowledge Discovery about Preferences

  • Authors:

  • Roman Słowiński

  • Affiliations:

  • Institute of Computing Science, Poznań University of Technology, Poznań 60-965 and Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland 01-447

  • Venue:
  • ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems
  • Year:
  • 2009

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Abstract

It is commonly acknowledged that a rational decision maker acts with respect to his/her value system so as to make the best decision. Confrontation of the value system of the decision maker with characteristics of possible decisions (objects) results in expression of preferences of the decision maker on the set of possible decisions. In order to support the decision maker, one must identify his/her preferences and recommend the most-preferred decision concerning either classification, or choice, or ranking. In this paper, we review multiple attribute and multiple criteria decision problems, as well as preference discovery from data describing some past decisions of the decision maker. The considered preference model has the form of a set of "if..., then... " decision rules induced from the data. To structure the data prior to induction, we use the Dominance-based Rough Set Approach (DRSA). DRSA is a methodology for reasoning about ordinal data, which extends the classical rough set approach by handling background knowledge about ordinal evaluations of objects and about monotonic relationships between these evaluations. The paper starts with an introduction to preference modeling in multiple attribute and multiple criteria decision problems, then presents the principles of DRSA, together with a didactic example, and concludes with a summary of characteristic features of DRSA in the context of preference modeling.