Overview
- Considers not only the regular attributes but also the criteria in the incomplete information systems
- Presents most of the important rough set models in the incomplete information systems
- Addresses the practical approaches to compute reducts in terms of these models
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents (6 chapters)
-
Front Matter
-
Indiscernibility Relation Based Rough Sets
-
Front Matter
-
-
Incomplete Information Systems and Rough Sets
-
Front Matter
-
-
Dominance-based Rough Sets and Incomplete Information Systems
-
Front Matter
-
-
Incomplete Information Systems and Multigranulation Rough Sets
-
Front Matter
-
-
Back Matter
Keywords
About this book
"Incomplete Information System and Rough Set Theory: Models and Attribute Reductions" covers theoretical study of generalizations of rough set model in various incomplete information systems. It discusses not only the regular attributes but also the criteria in the incomplete information systems. Based on different types of rough set models, the book presents the practical approaches to compute several reducts in terms of these models. The book is intended for researchers and postgraduate students in machine learning, data mining and knowledge discovery, especially for those who are working in rough set theory, and granular computing.
Dr. Xibei Yang is a lecturer at the School of Computer Science and Engineering, Jiangsu University of Science and Technology, China; Jingyu Yang is a professor at the School of Computer Science, Nanjing University of Science and Technology, China.
Authors and Affiliations
Bibliographic Information
Book Title: Incomplete Information System and Rough Set Theory
Book Subtitle: Models and Attribute Reductions
Authors: Xibei Yang, Jingyu Yang
DOI: https://doi.org/10.1007/978-3-642-25935-7
Publisher: Springer Berlin, Heidelberg
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Science Press,Beijng and Springer Berlin Heidelberg 2012
Hardcover ISBN: 978-3-642-25934-0Published: 07 May 2012
eBook ISBN: 978-3-642-25935-7Published: 15 December 2012
Edition Number: 1
Number of Pages: XIV, 232
Additional Information: Jointly published with Science Press.
Topics: Data Mining and Knowledge Discovery, Models and Principles, Artificial Intelligence, Database Management, Mathematical Logic and Formal Languages