Rough set based processing of inconsistent information in decision analysis

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