Rough Sets under Non-deterministic Information

Authors:
Michinori Nakata;Hiroshi Sakai
Affiliations:
Faculty of Management and Information Science, Josai International University, Chiba, Japan 283-8555;Department of Mathematics and Computer Aided Sciences, Faculty of Engineering, Kyushu Institute of Technology, Tobata, Japan 804-8550
Venue:
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
Year:
2009

Citing 14
Cited 0

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
Current Approaches to Handling Imperfect Information in Data and Knowledge Bases

IEEE Transactions on Knowledge and Data Engineering
Handling Missing Values in Rough Set Analysis of Multi-Attribute and Multi-Criteria Decision Problems

RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
On decomposition for incomplete data

Fundamenta Informaticae
Maximal consistent block technique for rule acquisition in incomplete information systems

Information Sciences: an International Journal
Effective Procedures for Handling Possible Equivalence Relations in Non-deterministic Information Systems

Fundamenta Informaticae
Applying Rough Sets to Information Tables Containing Probabilistic Values

MDAI '07 Proceedings of the 4th international conference on Modeling Decisions for Artificial Intelligence
Lower and upper approximations in data tables containing possibilistic information

Transactions on rough sets VII
Applying rough sets to data tables containing imprecise information under probabilistic interpretation

RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
Rough sets handling missing values probabilistically interpreted

RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Checking whether or not rough-set-based methods to incomplete data satisfy a correctness criterion

MDAI'05 Proceedings of the Second international conference on Modeling Decisions for Artificial Intelligence
Characteristic relations for incomplete data: a generalization of the indiscernibility relation

Transactions on Rough Sets IV
Set-valued information systems

Information Sciences: an International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

A method of possible equivalence classes, described in [14], is extended under non-deterministic information. The method considers both indiscernibility and discernibility of non-deterministic values by using possible equivalence classes. As a result, the method gives the same results as the method of possible worlds. Furthermore, maximal possible equivalences are introduced in order to effectively calculate rough approximations. We can use the method of possible equivalence classes to obtain rough approximations between arbitrary sets of attributes containing non-deterministic values.