Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat 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.
2018 (Engelska)Konferensbidrag, Publicerat paper (Refereegranskat)
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.

Ort, förlag, år, upplaga, sidor
IEEE Computer Society, 2018.
Nationell ämneskategori
Datavetenskap (datalogi)
Forskningsämne
data- och systemvetenskap
Identifikatorer
URN: urn:nbn:se:su:diva-161396OAI: oai:DiVA.org:su-161396DiVA, id: diva2:1258209
Konferens
IEEE International Conference on Data Mining, Singapore, November 17-20, 2018
Tillgänglig från: 2018-10-24 Skapad: 2018-10-24 Senast uppdaterad: 2019-04-16

Open Access i DiVA

Fulltext saknas i DiVA

Övriga länkar

arXiv:1809.05183

Sök vidare i DiVA

Av författaren/redaktören
Karlsson, IsakRebane, JonathanPapapetrou, Panagiotis
Av organisationen
Institutionen för data- och systemvetenskap
Datavetenskap (datalogi)

Sök vidare utanför DiVA

GoogleGoogle Scholar

urn-nbn

Altmetricpoäng

urn-nbn
Totalt: 34 träffar
RefereraExporteraLänk till posten
Permanent länk

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