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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 :2017.
Description:	xiii, 194 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Numeric Computing.
Online resource:	http://dx.doi.org/10.1007/978-981-10-4965-1
ISBN:	9789811049651$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. - Singapore :Springer Singapore :2017. - xiii, 194 p. :ill., digital ;24 cm.

Introduction to Feature Selection -- Background -- Rough Set Theory -- Advance Concepts in RST -- Rough Set Based Feature Selection Techniques -- Unsupervised Feature Selection using RST -- Critical Analysis of Feature Selection Algorithms -- RST Source Code.

This book provides a comprehensive introduction to Rough Set-based feature selection. It enables the reader to systematically study all topics of Rough Set Theory (RST) including the preliminaries, advanced concepts and feature selection using RST. In addition, 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. Rough Set Theory, proposed in 1982 by Zdzislaw Pawlak, is an area in constant development. Focusing on the classification and analysis of imprecise or uncertain information and knowledge, it has become a prominent tool for data analysis. Feature selection is one of the important applications of RST, and helps us select the features that provide us with the largest amount of useful information. The book offers a valuable reference guide for all students, researchers, and developers working in the areas of feature selection, knowledge discovery and reasoning with uncertainty, especially those involved in RST and granular computing.

ISBN: 9789811049651$q(electronic bk.)

Standard No.: 10.1007/978-981-10-4965-1doiSubjects--Topical Terms:

275524
Numeric Computing.

LC Class. No.: QA248

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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300 $a xiii, 194 p. : $b ill., digital ; $c 24 cm.
505 0 $a Introduction to Feature Selection -- Background -- Rough Set Theory -- Advance Concepts in RST -- Rough Set Based Feature Selection Techniques -- Unsupervised Feature Selection using RST -- Critical Analysis of Feature Selection Algorithms -- RST Source Code.
520 $a This book provides a comprehensive introduction to Rough Set-based feature selection. It enables the reader to systematically study all topics of Rough Set Theory (RST) including the preliminaries, advanced concepts and feature selection using RST. In addition, 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. Rough Set Theory, proposed in 1982 by Zdzislaw Pawlak, is an area in constant development. Focusing on the classification and analysis of imprecise or uncertain information and knowledge, it has become a prominent tool for data analysis. Feature selection is one of the important applications of RST, and helps us select the features that provide us with the largest amount of useful information. The book offers a valuable reference guide for all students, researchers, and developers working in the areas of feature selection, knowledge discovery and reasoning with uncertainty, especially those involved in RST and granular computing.
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