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Rough approximation by dominance relations
Author(s) -
Greco Salvatore,
Matarazzo Benedetto,
Slowinski Roman
Publication year - 2002
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.10014
Subject(s) - rough set , dominance based rough set approach , preference , decision rule , computer science , partition (number theory) , mathematics , rule induction , artificial intelligence , data mining , machine learning , statistics , combinatorics
In this article we are considering a multicriteria classification that differs from usual classificationproblems since it takes into account preference orders in the description of objects by condition and decisionattributes. To deal with multicriteria classification we propose to use a dominance‐based rough setapproach (DRSA). This approach is different from the classic rough set approach (CRSA)because it takes into account preference orders in the domains of attributes and in the set of decision classes.Given a set of objects partitioned into pre‐defined and preference‐ordered classes, the new roughset approach is able to approximate this partition by means of dominance relations (instead ofindiscernibility relations used in the CRSA). The rough approximation of this partition is a starting pointfor induction of if‐then decision rules. The syntax of these rules is adapted to represent preferenceorders. The DRSA keeps the best properties of the CRSA: it analyses only facts present in data, and possibleinconsistencies are not corrected. Moreover, the new approach does not need any prior discretization ofcontinuous‐valued attributes. In this article we characterize the DRSA as well as decision rules inducedfrom these approximations. The usefulness of the DRSA and its advantages over the CRSA are presented in a realstudy of evaluation of the risk of business failure. © 2002 John Wiley & Sons, Inc.