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Author Raza, Muhammad Summair, author
Title Understanding and using rough set based feature selection : Concepts, Techniques and Applications / by Muhammad Summair Raza, Usman Qamar
Imprint Singapore : Springer Singapore : Imprint: Springer, 2019
book jacket
Edition Second edition
Descript 1 online resource (xvi, 236 pages) : illustrations (some color), digital ; 24 cm
text txt rdacontent
computer c rdamedia
online resource cr rdacarrier
text file PDF rda
Note Introduction to Feature Selection -- Background -- Rough Set Theory -- Advance Concepts in Rough Set Theory -- Rough Set Theory Based Feature Selection Techniques -- Chapter 6: Unsupervised Feature Selection using RST -- Critical Analysis of Feature Selection Algorithms -- Dominance based Rough Set Approach -- Fuzzy Rough Sets -- Introduction to classicial Rough Set Based APIs Library -- Dominance Based Rough Set APIs library
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
Host Item Springer Nature eBook
Subject Rough sets
Artificial Intelligence
Information Systems Applications (incl. Internet)
Database Management
Data Mining and Knowledge Discovery
Numeric Computing
Alt Author Qamar, Usman, author
SpringerLink (Online service)
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