Rough set approach to incomplete information systems
Information Sciences: an International Journal
Rules in incomplete information systems
Information Sciences: an International Journal
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Boolean Reasoning for Decision Rules Generation
ISMIS '93 Proceedings of the 7th International Symposium on Methodologies for Intelligent Systems
Searching for Frequential Reducts in Decision Tables with Uncertain Objects
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
Fundamenta Informaticae
Pawlak's Information Systems in Terms of Galois Connections and Functional Dependencies
Fundamenta Informaticae - New Frontiers in Scientific Discovery - Commemorating the Life and Work of Zdzislaw Pawlak
Association reducts: boolean representation
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
Association reducts: complexity and heuristics
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
Approximate boolean reasoning: foundations and applications in data mining
Transactions on Rough Sets V
Attribute reduction in decision-theoretic rough set models
Information Sciences: an International Journal
Fast Discovery of Minimal Sets of Attributes Functionally Determining a Decision Attribute
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
FUN: Fast Discovery of Minimal Sets of Attributes Functionally Determining a Decision Attribute
Transactions on Rough Sets IX
Quick attribute reduction in inconsistent decision tables
Information Sciences: an International Journal
| Hi-index | 0.00 |
An essential notion in the theory of Rough Sets is a reduct, which is a minimal set of conditional attributes that preserves a required classification feature, e.g. respective values of an original or modified decision attribute. Certain decision reducts, generalized decision reducts, and membership distribution reducts belong to basic types of Rough Sets reducts. In our paper, we prove that reducts of these types are sets of conditional attributes functionally determining respective modifications of a decision attribute both in complete and incomplete information systems. However, we also prove that, unlike in the case of complete systems, the reducts in incomplete systems are not guaranteed to be minimal sets of conditional attributes that functionally determine respective modifications of the decision attribute.