Ordinal Versus Nominal Time Series Classification
Time series ordinal classification is one of the less studied problems in time series data mining. This problem consists in classifying time series with labels that show a natural order between them. In this paper, an approach is proposed based on the Sha
- PDF / 27,692,452 Bytes
- 240 Pages / 439.37 x 666.142 pts Page_size
- 74 Downloads / 194 Views
Vincent Lemaire · Simon Malinowski · Anthony Bagnall · Thomas Guyet · Romain Tavenard · Georgiana Ifrim (Eds.)
Advanced Analytics and Learning on Temporal Data 5th ECML PKDD Workshop, AALTD 2020 Ghent, Belgium, September 18, 2020 Revised Selected Papers
123
Lecture Notes in Artificial Intelligence Subseries of Lecture Notes in Computer Science
Series Editors Randy Goebel University of Alberta, Edmonton, Canada Yuzuru Tanaka Hokkaido University, Sapporo, Japan Wolfgang Wahlster DFKI and Saarland University, Saarbrücken, Germany
Founding Editor Jörg Siekmann DFKI and Saarland University, Saarbrücken, Germany
12588
More information about this subseries at http://www.springer.com/series/1244
Vincent Lemaire Simon Malinowski Anthony Bagnall Thomas Guyet Romain Tavenard Georgiana Ifrim (Eds.) •
•
•
•
•
Advanced Analytics and Learning on Temporal Data 5th ECML PKDD Workshop, AALTD 2020 Ghent, Belgium, September 18, 2020 Revised Selected Papers
123
Editors Vincent Lemaire Orange Labs Lannion, France Anthony Bagnall University of East Anglia Norwich, UK Romain Tavenard CNRS, LETG/IRISA University of Rennes 2 Rennes, France
Simon Malinowski Inria University of Rennes Rennes, France Thomas Guyet Agrocampus Ouest/IRISA Rennes, France Georgiana Ifrim University College Dublin Dublin, Ireland
ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notes in Artificial Intelligence ISBN 978-3-030-65741-3 ISBN 978-3-030-65742-0 (eBook) https://doi.org/10.1007/978-3-030-65742-0 LNCS Sublibrary: SL7 – Artificial Intelligence © Springer Nature Switzerland AG 2020 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, expressed 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. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Preface
Workshop Description The European Conference on Machine Learning and Principles and
