Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Multi-channel ECG classification using forests of randomized shapelet trees
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2015 (English)In: Proceedings of the 8th ACM International Conference on PErvasive Technologies Related to Assistive Environments, Association for Computing Machinery (ACM), 2015, 43Conference paper, Published paper (Refereed)
Abstract [en]

Data series of multiple channels occur at high rates and in massive quantities in several application domains, such as healthcare. In this paper, we study the problem of multi-channel ECG classification. We map this problem to multivariate data series classification and propose five methods for solving it, using a split-and-combine approach. The proposed framework is evaluated using three base-classifiers on real-world data for detecting Myocardial Infarction. Extensive experiments are performed on real ECG data extracted from the Physiobank data repository. Our findings emphasize the importance of selecting an appropriate base-classifier for multivariate data series classification, while demonstrating the superiority of the Random Shapelet Forest (0.825 accuracy) against competitor methods (0.664 accuracy for 1-NN under cDTW).

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2015. 43
Keyword [en]
Data series, classification, multi-channel
National Category
Information Systems
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-122846DOI: 10.1145/2769493.2769520ISBN: 978-1-4503-3452-5 (electronic)OAI: oai:DiVA.org:su-122846DiVA: diva2:868518
Conference
PETRA '15, 8th Pervasive Technologies Related to Assistive Environments, Corfu, Greece, July 01 - 03, 2015
Available from: 2015-11-11 Created: 2015-11-10 Last updated: 2017-04-28Bibliographically approved
In thesis
1. Order in the random forest
Open this publication in new window or tab >>Order in the random forest
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

In many domains, repeated measurements are systematically collected to obtain the characteristics of objects or situations that evolve over time or other logical orderings. Although the classification of such data series shares many similarities with traditional multidimensional classification, inducing accurate machine learning models using traditional algorithms are typically infeasible since the order of the values must be considered.

In this thesis, the challenges related to inducing predictive models from data series using a class of algorithms known as random forests are studied for the purpose of efficiently and effectively classifying (i) univariate, (ii) multivariate and (iii) heterogeneous data series either directly in their sequential form or indirectly as transformed to sparse and high-dimensional representations. In the thesis, methods are developed to address the challenges of (a) handling sparse and high-dimensional data, (b) data series classification and (c) early time series classification using random forests. The proposed algorithms are empirically evaluated in large-scale experiments and practically evaluated in the context of detecting adverse drug events.

In the first part of the thesis, it is demonstrated that minor modifications to the random forest algorithm and the use of a random projection technique can improve the effectiveness of random forests when faced with discrete data series projected to sparse and high-dimensional representations. In the second part of the thesis, an algorithm for inducing random forests directly from univariate, multivariate and heterogeneous data series using phase-independent patterns is introduced and shown to be highly effective in terms of both computational and predictive performance. Then, leveraging the notion of phase-independent patterns, the random forest is extended to allow for early classification of time series and is shown to perform favorably when compared to alternatives. The conclusions of the thesis not only reaffirm the empirical effectiveness of random forests for traditional multidimensional data but also indicate that the random forest framework can, with success, be extended to sequential data representations.

Place, publisher, year, edition, pages
Stockholm: Department of Computer and Systems Sciences, Stockholm University, 2017. 76 p.
Series
Report Series / Department of Computer & Systems Sciences, ISSN 1101-8526 ; 17-004
Keyword
Machine learning, random forest, ensemble, time series, data series, sequential data, sparse data, high-dimensional data
National Category
Computer and Information Science
Research subject
Computer and Systems Sciences
Identifiers
urn:nbn:se:su:diva-142052 (URN)978-91-7649-827-9 (ISBN)978-91-7649-828-6 (ISBN)
Public defence
2017-06-08, L30, NOD-huset, Borgarfjordsgatan 12, Stockholm, 13:00 (English)
Opponent
Supervisors
Funder
Swedish Foundation for Strategic Research , IIS11-0053
Available from: 2017-05-16 Created: 2017-04-24 Last updated: 2017-05-15Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full text

Search in DiVA

By author/editor
Karlsson, IsakPapapetrou, PanagiotisAsker, Lars
By organisation
Department of Computer and Systems Sciences
Information Systems

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 59 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf