Rich time series classification using temporal logic

Yoo, C; Belta, C

Rich time series classification using temporal logic

Yoo, C

Belta, C

Permalink

Publication Type:: Conference Proceeding
Citation:: Robotics: Science and Systems, 2017, 13
Issue Date:: 2017-01-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Published versionAdobe PDF (1.48 MB)

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Yoo, C https://orcid.org/0000-0002-2773-0527	en_US
dc.contributor.author	Belta, C	en_US
dc.date.issued	2017-01-01	en_US
dc.identifier.citation	Robotics: Science and Systems, 2017, 13	en_US
dc.identifier.isbn	9780992374730	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119654
dc.description.abstract	© 2017 MIT Press Journals. All rights reserved. Time series classification is an important task in robotics that is often solved using supervised machine learning. However, classifier models are typically not 'readable' in the sense that humans cannot intuitively learn useful information about the relationship between inputs and outputs. In this paper, we address the problem of rich time series classification where we propose a novel framework for finding a temporal logic classifier specified in a human-readable form. The classifier is represented as a signal temporal logic (STL) formula that is expressive in capturing spatial, temporal and logical relations from a continuous-valued dataset over time. In the framework, we first find a set of representative logical formulas from the raw dataset, and then construct an STL classifier using a treebased clustering algorithm. We show that the framework runs in polynomial time and validate it using simulated examples where our framework is significantly more efficient than the closest existing framework (up to 920 times faster).	en_US
dc.relation.ispartof	Robotics: Science and Systems	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Rich time series classification using temporal logic	en_US
dc.type	Conference Proceeding
utslib.citation.volume	13	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	13	en_US

Abstract:

© 2017 MIT Press Journals. All rights reserved. Time series classification is an important task in robotics that is often solved using supervised machine learning. However, classifier models are typically not 'readable' in the sense that humans cannot intuitively learn useful information about the relationship between inputs and outputs. In this paper, we address the problem of rich time series classification where we propose a novel framework for finding a temporal logic classifier specified in a human-readable form. The classifier is represented as a signal temporal logic (STL) formula that is expressive in capturing spatial, temporal and logical relations from a continuous-valued dataset over time. In the framework, we first find a set of representative logical formulas from the raw dataset, and then construct an STL classifier using a treebased clustering algorithm. We show that the framework runs in polynomial time and validate it using simulated examples where our framework is significantly more efficient than the closest existing framework (up to 920 times faster).

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/119654