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Understanding and using rough set ba...

Qamar, Usman.

Understanding and using rough set based feature selectionConcepts, Techniques and Applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Understanding and using rough set based feature selectionby Muhammad Summair Raza, Usman Qamar.
Reminder of title:	Concepts, Techniques and Applications /
Author:	Raza, Muhammad Summair.
other author:	Qamar, Usman.
Published:	Singapore :Springer Singapore :2019.
Description:	xvi, 236 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Rough sets.
Online resource:	https://doi.org/10.1007/978-981-32-9166-9
ISBN:	9789813291669$q(electronic bk.)

Understanding and using rough set based feature selectionConcepts, Techniques and Applications /
Raza, Muhammad Summair.

Understanding and using rough set based feature selectionConcepts, Techniques and Applications /[electronic resource] :by Muhammad Summair Raza, Usman Qamar. - Second edition. - Singapore :Springer Singapore :2019. - xvi, 236 p. :ill. (some col.), digital ;24 cm.

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.

ISBN: 9789813291669$q(electronic bk.)

Standard No.: 10.1007/978-981-32-9166-9doiSubjects--Topical Terms:

231623
Rough sets.

LC Class. No.: QA248 / .R39 2019

Dewey Class. No.: 511.322

Understanding and using rough set based feature selectionConcepts, Techniques and Applications /
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245 1 0 $a Understanding and using rough set based feature selection $h [electronic resource] : $b Concepts, Techniques and Applications / $c by Muhammad Summair Raza, Usman Qamar.
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505 0 $a 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.
520 $a 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.
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