Incomplete information system and rough set theory: models and attribute reductions

'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 tocompute 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. 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. INDICE: Part I. Indiscernibility Relation Based Rough Sets. Part II. Incomplete Information Systems and Rough Sets.- Part III. Dominance-based Rough Sets and Incomplete Information Systems. Part IV. Incomplete Information Systems and Multigranulation Rough Sets.

• ISBN: 978-3-642-25934-0
• Editorial: Springer Berlin Heidelberg
• Encuadernacion: Cartoné
• Páginas: 225
• Fecha Publicación: 29/02/2012
• Nº Volúmenes: 1
• Idioma: Inglés