Interpretability of Computational Intelligence-Based Regression Models
The key idea of this book is that hinging hyperplanes, neural networks and support vector machines can be transformed into fuzzy models, and interpretability of the resulting rule-based systems can be ensured by special model reduction and visualization t
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Tamás Kenesei János Abonyi
Interpretability of Computational IntelligenceBased Regression Models 123
SpringerBriefs in Computer Science
More information about this series at http://www.springer.com/series/10028
Tamás Kenesei János Abonyi •
Interpretability of Computational Intelligence-Based Regression Models
123
Tamás Kenesei Department of Process Engineering University of Pannonia Veszprém Hungary
János Abonyi Department of Process Engineering University of Pannonia Veszprém Hungary
ISSN 2191-5768 ISSN 2191-5776 (electronic) SpringerBriefs in Computer Science ISBN 978-3-319-21941-7 ISBN 978-3-319-21942-4 (eBook) DOI 10.1007/978-3-319-21942-4 Library of Congress Control Number: 2015947805 Springer Cham Heidelberg New York Dordrecht London © The Author(s) 2015 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. Printed on acid-free paper Springer International Publishing AG Switzerland is part of Springer Science+Business Media (www.springer.com)
Preface
Data-driven regression models such as hinging hyperplanes, neural networks and support vector machines are widely applied in control, optimization, and process monitoring. If we had some insight to these black boxes we could have the possibility to validate these models, extract hidden information about relationships among process variables, and support model identification by incorporating prior knowledge. The key idea of this book is that hinging hyperplanes, neural networks, and support vector machines can be transformed into fuzzy models, and interpretability of the resulting rule-based systems can be ensured by special model reduction and visualization techniques. The first part of the book deals with the identification of hinging hyperplanebased regression trees. The operating regime of the model is recursively partitioned by a novel fuzzy c-regression clustering-based technique. The resultant compact regression tree consists of local linear models whose model structure is favored in model-based control solutions
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