Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Explainable time series tweaking via irreversible and reversible temporal transformations
Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.ORCID-id: 0000-0002-4632-4815
2018 (engelsk)Inngår i: 2018 IEEE International Conference on Data Mining (ICDM): Proceedings, IEEE, 2018, s. 207-216Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Time series classification has received great attention over the past decade with a wide range of methods focusing on predictive performance by exploiting various types of temporal features. Nonetheless, little emphasis has been placed on interpretability and explainability. In this paper, we formulate the novel problem of explainable time series tweaking, where, given a time series and an opaque classifier that provides a particular classification decision for the time series, we want to find the minimum number of changes to be performed to the given time series so that the classifier changes its decision to another class. We show that the problem is NP-hard, and focus on two instantiations of the problem, which we refer to as reversible and irreversible time series tweaking. The classifier under investigation is the random shapelet forest classifier. Moreover, we propose two algorithmic solutions for the two problems along with simple optimizations, as well as a baseline solution using the nearest neighbor classifier. An extensive experimental evaluation on a variety of real datasets demonstrates the usefulness and effectiveness of our problem formulation and solutions.

sted, utgiver, år, opplag, sider
IEEE, 2018. s. 207-216
Serie
Proceedings IEEE International Conference on Data Mining, ISSN 1550-4786, E-ISSN 2374-8486
HSV kategori
Forskningsprogram
data- och systemvetenskap
Identifikatorer
URN: urn:nbn:se:su:diva-161396DOI: 10.1109/ICDM.2018.00036ISI: 000464691700022ISBN: 978-1-5386-9160-1 (tryckt)ISBN: 978-1-5386-9159-5 (digital)OAI: oai:DiVA.org:su-161396DiVA, id: diva2:1258209
Konferanse
IEEE International Conference on Data Mining, Singapore, November 17-20, 2018
Tilgjengelig fra: 2018-10-24 Laget: 2018-10-24 Sist oppdatert: 2020-04-24bibliografisk kontrollert

Open Access i DiVA

Fulltekst mangler i DiVA

Andre lenker

Forlagets fulltekstarXiv:1809.05183

Søk i DiVA

Av forfatter/redaktør
Karlsson, IsakRebane, JonathanPapapetrou, Panagiotis
Av organisasjonen

Søk utenfor DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric

doi
isbn
urn-nbn
Totalt: 46 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf