Incomplete Information System and Rough Set Theory : Models and Attribute Reductions

Xibei Yang

ISBN 10: 3642259340 / ISBN 13: 9783642259340

Editore: Springer, Berlin, Springer Berlin Heidelberg, Science Press, Springer, 2012

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Druck auf Anfrage Neuware - Printed after ordering - '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.

Codice articolo 9783642259340

Dati bibliografici

• Titolo: Incomplete Information System and Rough Set Theory : Models and Attribute Reductions
• Editore: Springer, Berlin, Springer Berlin Heidelberg, Science Press, Springer
• Data di pubblicazione: 2012
• Lingua: Inglese
• ISBN 10: 3642259340
• ISBN 13: 9783642259340
• Rilegatura: Buch
• Condizione: Neu
• Dimensioni: 235 L x 155 A x 35 P (millimetri) 235 millimetri di larghezza per 155 millimetri di altezza per 35 millimetri di profondità
• Peso: 476 grammi

Descrizione articolo

Riassunto

"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.

Contenuti

Part 1 Rough Sets in Complete Information System.- Indiscernibility Relation Based Rough Sets.- Dominance-based Rough Set Approach.- Part 2 Incomplete Information System with Unknown Values.- Generalized Binary Relations Based Rough sets.- Neighborhood Systems and Rough Sets.- Dominance-based Rough Set in incomplete system with “do not care” unknown values.- Dominance-based Rough Set in incomplete system with lost unknown values.- Rough Sets in Generalized Incomplete Information System.- Part 3 Set-valued And Interval-valued Information Systems.- Rough Sets And Dominance-based Rough Sets in Set-valued Information System.- Rough Sets And Dominance-based Rough Sets in Interval-valued Information System.