Understanding and Using Rough Set Based Feature Selection: Concepts, Techniques and Applications
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 add
- PDF / 7,110,731 Bytes
- 200 Pages / 439.42 x 683.15 pts Page_size
- 3 Downloads / 151 Views
Understanding and Using Rough Set Based Feature Selection: Concepts, Techniques and Applications
Understanding and Using Rough Set Based Feature Selection: Concepts, Techniques and Applications
Muhammad Summair Raza • Usman Qamar
Understanding and Using Rough Set Based Feature Selection: Concepts, Techniques and Applications
Muhammad Summair Raza Department of Computer Engineering, College of Electrical & Mechanical Engineering National University of Sciences and Technology (NUST) Rawalpindi, Pakistan
Usman Qamar Department of Computer Engineering, College of Electrical & Mechanical Engineering National University of Sciences and Technology (NUST) Rawalpindi, Pakistan
ISBN 978-981-10-4964-4 ISBN 978-981-10-4965-1 DOI 10.1007/978-981-10-4965-1
(eBook)
Library of Congress Control Number: 2017942777 © The Editor(s) (if applicable) and The Author(s) 2017 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Printed on acid-free paper This Springer imprint is published by Springer Nature The registered company is Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore
Preface
Rough set theory (RST) has become a prominent tool for data science in various domains due to its analysis-friendly nature. From scientific discovery to business intelligence, both practitioners and scientists are using RST in various domains. Feature selection (FS) community is one to name. Various algorithms have been proposed in literature using RST, and a lot of search is still in progress. For any practitioner and research community, this book provides a strong foundation of the concepts of RST and FS. It starts with the introduction of feature selection and rough set theory (along with the working examples) right to the advanced concepts. Sufficient explanation is provided for each concept so that th
Data Loading...
