TY  - BOOK
AU  - Peters,James F.
AU  - Skowron,Andrzej
AU  - Li,Tianrui
AU  - Yang,Yan
AU  - Yao,JingTao
AU  - Nguyen,Hung Son
ED  - SpringerLink (Online service)
TI  - Transactions on Rough Sets XVIII
T2  - Lecture Notes in Computer Science,
SN  - 9783662446805
AV  - Q337.5 
U1  - 006.4 23
PY  - 2014///
CY  - Berlin, Heidelberg
PB  - Springer Berlin Heidelberg, Imprint: Springer
KW  - Computer Science
KW  - Electronic data processing
KW  - Artificial intelligence
KW  - Optical pattern recognition
KW  - Pattern Recognition
KW  - Numeric Computing
KW  - Artificial Intelligence (incl. Robotics)
KW  - Mathematical Logic and Formal Languages
N1  - On the Intuitionistic Fuzzy Topological Structures of Rough Intuitionistic Fuzzy Sets -- Feature Selection with Positive Region Constraint for Test-cost-sensitive Data -- A Rough Neurocomputing approach for Illumination Invariant Face Recognition System -- Variable Precision Multigranulation Rough Set and Attributes Reduction -- Three-way Decisions Versus Two-way Decisions on Filtering Spam Email -- A Three-way Decisions Approach to Density-based Overlapping Clustering -- Three-way Decisions in Stochastic Decision-Theoretic Rough Sets
N2  - The LNCS journal Transactions on Rough Sets is devoted to the entire spectrum of rough sets related issues, from logical and mathematical foundations, through all aspects of rough set theory and its applications, such as data mining, knowledge discovery, and intelligent information processing, to relations between rough sets and other approaches to uncertainty, vagueness, and incompleteness, such as fuzzy sets and theory of evidence. Volume XVIII includes extensions of papers from the Joint Rough Set Symposium (JRS 2012), which was held in Chengdu, China, in August 2012. The seven papers that constitute this volume deal with topics such as: rough fuzzy sets, intuitionistic fuzzy sets, multi-granulation rough sets, decision-theoretic rough sets, three-way decisions and their applications in attribute reduction, feature selection, overlapping clustering, data mining, cost-sensitive learning, face recognition, and spam filtering
UR  - http://dx.doi.org/10.1007/978-3-662-44680-5
ER  -