Author
Wei-Zhi Wu
Other affiliations: Xi'an Jiaotong University
Bio: Wei-Zhi Wu is an academic researcher from Zhejiang Ocean University. The author has contributed to research in topics: Rough set & Fuzzy set operations. The author has an hindex of 37, co-authored 142 publications receiving 5769 citations. Previous affiliations of Wei-Zhi Wu include Xi'an Jiaotong University.
Papers published on a yearly basis
Papers
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TL;DR: This paper presents a general framework for the study of fuzzy rough sets in which both constructive and axiomatic approaches are used and the connections between fuzzy relations and fuzzy rough approximation operators are examined.
568 citations
TL;DR: This paper presents a general framework for the study of rough set approximation operators in fuzzy environment in which both constructive and axiomatic approaches are used.
449 citations
TL;DR: It is proved that for some special thresholds, β lower distribution reduct is equivalent to the maximum distribution reduction reduct, whereas β upper distribution reduCT is equivalents to the possible reduct.
388 citations
TL;DR: Granular structure of concept lattices with application in knowledge reduction in formal concept analysis is examined in this paper and knowledge hidden in such a context is unraveled in the form of compact implication rules.
Abstract: Granular computing and knowledge reduction are two basic issues in knowledge representation and data mining. Granular structure of concept lattices with application in knowledge reduction in formal concept analysis is examined in this paper. Information granules and their properties in a formal context are first discussed. Concepts of a granular consistent set and a granular reduct in the formal context are then introduced. Discernibility matrices and Boolean functions are, respectively, employed to determine granular consistent sets and calculate granular reducts in formal contexts. Methods of knowledge reduction in a consistent formal decision context are also explored. Finally, knowledge hidden in such a context is unraveled in the form of compact implication rules.
311 citations
TL;DR: Two new quantitative measures, “random certainty factor” and "random coverage factor" are associated with each decision rule are further proposed to explain relationships between the condition and decision parts of a rule in incomplete decision tables.
297 citations
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TL;DR: The basic concepts of rough set theory are presented and some rough set-based research directions and applications are pointed out, indicating that the rough set approach is fundamentally important in artificial intelligence and cognitive sciences.
2,004 citations
TL;DR: This paper provides an analysis of three-way decision rules in the classical rough set model and the decision-theoretic rough set models, enriched by ideas from Bayesian decision theory and hypothesis testing in statistics.
1,088 citations
TL;DR: It is shown that some of the properties of Pawlak's rough set theory are special instances of those of MGRS, and several important measures are presented, which are re-interpreted in terms of a classic measure based on sets, the Marczewski-Steinhaus metric and the inclusion degree measure.
604 citations
TL;DR: A theoretic framework based on rough set theory, called positive approximation, is introduced, which can be used to accelerate a heuristic process of attribute reduction, and several representative heuristic attribute reduction algorithms inrough set theory have been enhanced.
588 citations