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Cover Art
E-RESOURCE
Author Raza, Muhammad Summair, author.

Title Understanding and using rough set based feature selection : concepts, techniques and applications / Muhammad Summari Raza, Usman Qamar.

Published Singapore : Springer, [2017]

Copies

Location Call No. Status
 UniM INTERNET resource    AVAILABLE
Physical description 1 online resource (xiii, 194 pages) : illustrations (some color)
Series Springer Computer Science eBooks 2017 English+International
Bibliography Includes bibliographical references.
Contents 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.
Summary 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.
Other author Qamar, Usman, author.
SpringerLink issuing body.
Subject Rough sets.
Electronic books.
ISBN 9789811049651 (electronic bk.)
9811049653 (electronic bk.)
9789811049644 (print)
9811049645
9789811049644
Standard Number 10.1007/978-981-10-4965-1