Rough set based processing of inconsistent information in decision analysis

• Autorzy: Słowiński R. , Stefanowski J. , Greco S. , Matarazzo B.
• Języki publikacji: EN

Abstrakty

EN

Inconsistent information is one of main difficulties in the explanation and recommendation tasks of decision analysis. We distinguish two kinds of such information inconsistencies : the first is related to indiscernibility of objects described by attributes defined in nominal or ordinal scales, and the other follows from violation of the dominance principle among attributes defined on preference ordered ordinal or cardinal scales, i.e. among criteria. In this paper we discuss how these two kinds of inconsistencies are handled by a new approach based on the rough sets theory. Combination of this theory with inductive learning techniques leads to generation of decision rules from rough approximations of decision classes. Particular attention is paid to numerical attribute scales and preference-ordered scales of criteria, and their influence on the syntax of induced decision rules.

Słowa kluczowe

EN

classification; decision analysis; knowledge based systems; multi-criteria decision analysis; rough sets; rule induction

PL

klasyfikacja; kombinatoryka; teoria decyzji; teoria gier

• Wydawca: Systems Research Institute, Polish Academy of Sciences
• Czasopismo: Control and Cybernetics
• Rocznik: 2000
• Tom: Vol. 29, no 1
• Strony: 379--404
• Opis fizyczny: Bibliogr. 56 poz.,

Twórcy

autor

Słowiński R.

autor

Stefanowski J.

autor

Greco S.

autor

Matarazzo B.

• Institute of Computing Science, Poznań University of Technology, 60-965 Poznań, Poland,