Understanding and Using Rough Set Based Feature Selection: Concepts, Techniques and Applications
Language: English
Published by Springer, Springer Sep 2020, 2020
- Softcover
- New
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germanybuchversandmimpf2000
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This item is printed on demand - Print on Demand Titel. Neuware -This book provides a comprehensive introduction to rough set-based feature selection. Rough set theory, first proposed by Zdzislaw Pawlak in 1982, continues to evolve. Concerned with the classification and analysis of imprecise or uncertain information and knowledge, it has become a prominent tool for data analysis, and enables the reader to systematically study all topics in rough set theory (RST) including preliminaries, advanced concepts, and feature selection using RST. The book is supplemented with an RST-based API library that can be used to implement several RST concepts and RST-based feature selection algorithms.The book provides an essential reference guide for students, researchers, and developers working in the areas of feature selection, knowledge discovery, and reasoning with uncertainty, especially those who are working in RST and granular computing. The primary audience of this book is the research community using rough set theory (RST) to perform feature selection (FS) on large-scale datasets in various domains. However, any community interested in feature selection such as medical, banking, and finance can also benefit from the book.This second edition also covers the dominance-based rough set approach and fuzzy rough sets. The dominance-based rough set approach (DRSA) is an extension of the conventional rough set approach and supports the preference order using the dominance principle. In turn, fuzzy rough sets are fuzzy generalizations of rough sets. An API library for the DRSA is also provided with the second edition of the book.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 252 pp. Englisch.…
Seller Inventory # 9789813291683
- Title
- Understanding and Using Rough Set Based Feature Selection: Concepts, Techniques and Applications
- Author
- Muhammad Summair Raza
- Publisher
- Springer, Springer Sep 2020
- Publication year
- 2020
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 9813291680
- ISBN 13
- 9789813291683
- Edition
- 2nd Edition
- Item weight
- 388 grams
- Dimensions
- 235x155x14 mm
This book provides a comprehensive introduction to rough set-based feature selection. Rough set theory, first proposed by Zdzislaw Pawlak in 1982, continues to evolve. Concerned with the classification and analysis of imprecise or uncertain information and knowledge, it has become a prominent tool for data analysis, and enables the reader to systematically study all topics in rough set theory (RST) including preliminaries, advanced concepts, and feature selection using RST. The book is supplemented with an RST-based API library that can be used to implement several RST concepts and RST-based feature selection algorithms.
The book provides an essential reference guide for students, researchers, and developers working in the areas of feature selection, knowledge discovery, and reasoning with uncertainty, especially those who are working in RST and granular computing. The primary audience of this book is the research community using rough set theory (RST) to perform feature selection (FS) on large-scale datasets in various domains. However, any community interested in feature selection such as medical, banking, and finance can also benefit from the book.
This second edition also covers the dominance-based rough set approach and fuzzy rough sets. The dominance-based rough set approach (DRSA) is an extension of the conventional rough set approach and supports the preference order using the dominance principle. In turn, fuzzy rough sets are fuzzy generalizations of rough sets. An API library for the DRSA is also provided with the second edition of the book.
"Synopsis" may belong to another edition of this title.
About the Author
Dr. Muhammad Summair Raza holds a Ph.D. specialization in Software Engineering from the National University of Science and Technology (NUST), Pakistan. He completed his M.S. at the International Islamic University, Pakistan, in 2009. He is also associated with the Virtual University of Pakistan as an Assistant Professor. Having published various papers in international-level journals and conference proceedings, his research interests include Feature Selection, Rough Set Theory and Trend Analysis.
Dr. Usman Qamar has over 15 years of experience in data engineering in both academia and industry. He holds a Master’s in Computer Systems Design from the University of Manchester Institute of Science and Technology (UMIST), UK, as well as an M.Phil. and Ph.D. in Computer Science from the University of Manchester, UK. Dr Qamar’s research expertise is in Data and Text Mining, Expert Systems, Knowledge Discovery, and Feature Selection, areas in which he has published extensively. He is currently a Tenured Associate Professor at the Department of Computer & Software Engineering, National University of Sciences and Technology (NUST), Pakistan, where he also heads the Knowledge and Data Engineering Research Centre (KDRC).
"About the title" may belong to another edition of this title.
buchversandmimpf2000
Emtmannsberg, BAYE, Germany
AbeBooks seller since January 23, 2017
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