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Rough set analysis of preference-ordered data

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)

The paper is devoted to knowledge discovery from data, taking into account prior knowledge about preference semantics in patterns to be discovered. The data concern a set of situations (objects, states, examples) described by a set of attributes (properties, features, characteristics). The attributes are, in general, divided into condition and decision attributes, corresponding to input and output of a situation. The situations are partitioned by decision attributes into decision classes. A pattern discovered from the data has a symbolic form of decision rule or decision tree. In many practical problems, some condition attributes are defined on preference-ordered scales and the decision classes are also preference-ordered. The known methods of knowledge discovery ignore, unfortunately, this preference information, taking thus a risk of drawing wrong patterns. To deal with preference-ordered data we propose to use a new approach called Dominance-based Rough Set Approach (DRSA). Given a set of situations described by at least one condition attribute with preference-ordered scale and partitioned into preference-ordered classes, the new rough set approach is able to approximate this partition by means of dominance relations. 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 analyses only facts present in data and possible inconsistencies are identified. It preserves the concept of granular computing, however, the granules are dominance cones in evaluation space, and not bounded sets. It is also concordant with the paradigm of computing with words, as it exploits ordinal, and not necessarily cardinal, character of data.
Original languageEnglish
Title of host publicationRough sets and current trends in computing
Subtitle of host publicationthird international conference, RSCTC 2002 Malvern, PA, USA, October 14–16, 2002 proceedings
EditorsJ. J. Alpigini, J. F. Peters, A. Skowron, N. Zhong
Place of PublicationBerlin, Germany
PublisherSpringer
Pages44-59
Number of pages15
Volume2475
Edition2475
ISBN (Print)9783540442745
DOIs
Publication statusPublished - 2002

Publication series

NameLecture notes in computer science
PublisherSpringer
Number2475

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