A relational perspective of attribute reduction in rough set-based data analysis

Tuan Fang Fan, Churn Jung Liau*, Duen-Ren Liu

*Corresponding author for this work

Research output: Contribution to journalArticle

11 Scopus citations

Abstract

Attribute reduction is very important in rough set-based data analysis (RSDA) because it can be used to simplify the induced decision rules without reducing the classification accuracy. The notion of reduct plays a key role in rough set-based attribute reduction. In rough set theory, a reduct is generally defined as a minimal subset of attributes that can classify the same domain of objects as unambiguously as the original set of attributes. Nevertheless, from a relational perspective, RSDA relies on a kind of dependency principle. That is, the relationship between the class labels of a pair of objects depends on component-wise comparison of their condition attributes. The larger the number of condition attributes compared, the greater the probability that the dependency will hold. Thus, elimination of condition attributes may cause more object pairs to violate the dependency principle. Based on this observation, a reduct can be defined alternatively as a minimal subset of attributes that does not increase the number of objects violating the dependency principle. While the alternative definition coincides with the original one in ordinary RSDA, it is more easily generalized to cases of fuzzy RSDA and relational data analysis.

Original languageEnglish
Pages (from-to)270-278
Number of pages9
JournalEuropean Journal of Operational Research
Volume213
Issue number1
DOIs
StatePublished - 16 Aug 2011

Keywords

  • Attribute reduction
  • Decision analysis
  • Fuzzy sets
  • Relational information system
  • Rough sets

Fingerprint Dive into the research topics of 'A relational perspective of attribute reduction in rough set-based data analysis'. Together they form a unique fingerprint.

  • Cite this