Data mining tasks and methods: Classification: multicriteria classification

  • Authors:

  • Salvatore Greco;Benedetto Matarazzo;Roman Slowinski

  • Affiliations:

  • Associate Professor of Decision Theory, Faculty of Economics, University of Catania, Italy;Professor of Financial Mathematics, University of Catania, Italy;Professor of Decision Science, Head of the Laboratory of Intelligent Decision Support Systems, Institute of Computing Science, Poznan University of Technology, Poland

  • Venue:
  • Handbook of data mining and knowledge discovery
  • Year:
  • 2002

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Abstract

In this article we consider multicriteria classification, which differs from usual classification problems since it takes into account preference orders in the description of objects by condition and decision attributes. The well-known methods of knowledge discovery do not use information about preference orders in multicriteria classification. It is worthwhile, however, to take this information into account as many practical problems involve evaluation of objects on preference-ordered domains. To deal with multicriteria classification we propose to use a dominance-based rough set approach (DRSA). This approach is different from the classical 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 predefined and preference-ordered classes, the new rough set approach is able to approximate this partition by means of dominance relations (instead of indiscernibility relations used in the CRSA). The rough approximation of this partition is a starting point for induction of "if..., then..." decision rules. The syntax of these rules is adapted to represent preference orders. The DRSA keeps the best properties of the CRSA: it only analyzes facts present in data and possible inconsistencies are not corrected. Moreover, the new approach does not need any prior discretization of continuous-valued attributes. The usefulness of the DRSA and its advantages over the CRSA are presented in a real study of evaluation of the risk of business failure.