Rough Sets and Knowledge Technology

9th International Conference, RSKT 2014, Shanghai, China, October 24-26, 2014, Proceedings

Duoqian Miao, Witold Pedrycz, Dominik Ślȩzak, Georg Peters, Qinghua Hu, Ruizhi Wang (Herausgeber)

Buch | Softcover

XXVIII, 867 Seiten

2014
Springer International Publishing (Verlag)
978-3-319-11739-3 (ISBN)

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This book constitutes the thoroughly refereed conference proceedings of the 9th International Conference on Rough Sets and Knowledge Technology, RSKT 2014, held in Shanghai, China, in October 2014. The 70 papers presented were carefully reviewed and selected from 162 submissions. The papers in this volume cover topics such as foundations and generalizations of rough sets, attribute reduction and feature selection, applications of rough sets, intelligent systems and applications, knowledge technology, domain-oriented data-driven data mining, uncertainty in granular computing, advances in granular computing, big data to wise decisions, rough set theory, and three-way decisions, uncertainty, and granular computing.

Foundations and Generalizations of Rough Sets.- Attribute Reduction and Feature Selection.- Applications of Rough Sets.- Intelligent Systems and Applications.- Knowledge Technology.- Domain-Oriented Data-Driven Data Mining.- Uncertainty in Granular Computing.- Advances in Granular Computing.- Big Data to Wise Decisions.- Rough Set Theory.- Three-Way Decisions, Uncertainty, and Granular Computing.

Erscheint lt. Verlag	13.10.2014
Reihe/Serie	Lecture Notes in Artificial Intelligence
Reihe/Serie	Lecture Notes in Computer Science
Zusatzinfo	XXVIII, 867 p. 178 illus.
Verlagsort	Cham
Sprache	englisch
Maße	155 x 235 mm
Gewicht	1347 g
Themenwelt	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
Schlagworte	Approximation • Big Data • Data Mining • Decision Rules • Decision Systems • Granular Computing • Knowledge Representation and Reasoning • machine learning • Neural networks • pattern recognition • Recommender System • Rough Sets • three-way decisions • uncertainty analysis • uncertainty measures • web-based learning
ISBN-10	3-319-11739-4 / 3319117394
ISBN-13	978-3-319-11739-3 / 9783319117393
Zustand	Neuware