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Paper Abstract and Keywords
Presentation 2019-06-17 17:00
Adaptive Discretization based Predictive Sequence Mining for Continuous Time Series
Yoshikazu Shibahara, Takuto Sakuma (NIT), Ichiro Takeuchi (NIT/RIKEN/NIMS), Masayuki Karasuyama (NIT/NIMS) IBISML2019-9
Abstract (in Japanese) (See Japanese page) 
(in English) In recent years, improvement of sensor performance and spread of portable devices such as smartphones enable us to easily collect time-series data.

Thus, it is an important task to extract valuable information from time series-data.

In this research, we propose a method extracting a class specific patterns from time-series data by using an adaptive discretization algorithm for a continuous feature space.

Conventional approaches need to define a symbolized representation of the original continuous time-series data beforehand.

Our approach can construct a sparse linear model by selecting important patterns from a variety of possible symbolizations.

The proposed method efficiently deals with a huge number of patterns by combining a safe-screening technique and sequence pattern mining.

Our numerical experiments demonstrate effectiveness of our approach through several benchmark datasets.
Keyword (in Japanese) (See Japanese page) 
(in English) Continuous valued sequence data / sparse modeling / sequence mining / / / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 89, IBISML2019-9, pp. 57-64, June 2019.
Paper # IBISML2019-9 
Date of Issue 2019-06-10 (IBISML) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF IBISML2019-9

Conference Information
Committee NC IBISML IPSJ-MPS IPSJ-BIO  
Conference Date 2019-06-17 - 2019-06-19 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa Institute of Science and Technology 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Neurocomputing, Machine Learning Approach to Biodata Mining, and General 
Paper Information
Registration To IBISML 
Conference Code 2019-06-NC-IBISML-MPS-BIO 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Adaptive Discretization based Predictive Sequence Mining for Continuous Time Series 
Sub Title (in English)  
Keyword(1) Continuous valued sequence data  
Keyword(2) sparse modeling  
Keyword(3) sequence mining  
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1st Author's Name Yoshikazu Shibahara  
1st Author's Affiliation Nagoya Institute of Technolog (NIT)
2nd Author's Name Takuto Sakuma  
2nd Author's Affiliation Nagoya Institute of Technolog (NIT)
3rd Author's Name Ichiro Takeuchi  
3rd Author's Affiliation Nagoya Institute of Technology/RIKEN Center for Advanced Intelligence Project/National Institute for Materials Science (NIT/RIKEN/NIMS)
4th Author's Name Masayuki Karasuyama  
4th Author's Affiliation Nagoya Institute of Technology/National Institute for Materials Science (NIT/NIMS)
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Speaker Author-1 
Date Time 2019-06-17 17:00:00 
Presentation Time 25 minutes 
Registration for IBISML 
Paper # IBISML2019-9 
Volume (vol) vol.119 
Number (no) no.89 
Page pp.57-64 
#Pages 8 
Date of Issue 2019-06-10 (IBISML) 

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