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Rough Set Analysis of preference-ordered data. Retrieved March 2011. http://roughsets.home.pl/IRSS/RSCTC_02_final pliki/ v3_document.htm (2002)
| Content Provider | CiteSeerX |
|---|---|
| Author | Matarazzo, Benedetto Greco, Salvatore |
| Abstract | Abstract. 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 preferenceordered 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. 1 How Prior Knowledge Influences Knowledge |
| File Format | |
| Publisher Date | 2002-01-01 |
| Access Restriction | Open |
| Subject Keyword | Rough Set Analysis Decision Rule Evaluation Space Wrong Pattern Many Practical Problem Knowledge Discovery Ignore Decision Class Granular Computing Dominance-based Rough Set Approach Preference Semantics Preferenceordered Class Dominance Cone Decision Tree Decision Attribute Rough Approximation Condition Attribute Dominance Relation Preference Order Symbolic Form Pl Irs Rsctc_02_final Pliki V3_document Data Concern New Rough Preference Information Preference-ordered Scale Known Method Prior Knowledge Influence Knowledge Possible Inconsistency Preference-ordered Data |
| Content Type | Text |
| Resource Type | Article |
