Multiple Fuzzy Classification Systems
Fuzzy classifiers are important tools in exploratory data analysis, which is a vital set of methods used in various engineering, scientific and business applications. Fuzzy classifiers use fuzzy rules and do not require assumptions common to statistical clas
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288
Rafał Scherer
Multiple Fuzzy Classification Systems
ABC
Author Dr. Rafał Scherer Department of Computer Engineering Czestochowa University of Technology Poland
ISSN 1434-9922 e-ISSN 1860-0808 ISBN 978-3-642-30603-7 e-ISBN 978-3-642-30604-4 DOI 10.1007/978-3-642-30604-4 Springer Heidelberg New York Dordrecht London Library of Congress Control Number: 2012938369 c Springer-Verlag Berlin Heidelberg 2012 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. Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher’s location, in its current version, and permission for use must always be obtained from Springer. Permissions for use may be obtained through RightsLink at the Copyright Clearance Center. Violations are liable to prosecution under the respective Copyright Law. 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. While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
Preface
Exploratory data analysis is a vital set of methods used in various engineering, scientific and business applications. Fuzzy classifiers are important tools in this growing field. They use fuzzy rules and do not require assumptions common to statistical classification. Rough set theory is useful when data sets are incomplete. It defines a formal approximation of crisp sets by providing the lower and the upper approximation of the original set. Systems based on rough sets have natural ability to work on such data and we do not have to preprocess incomplete vectors before classification. To achieve better performance than existing machine learning systems, we combine them in ensembles. Such ensembles consists of a finite set of learning models,
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