Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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
Outliers detection using an iterative strategy for semi‐supervised learning
Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark.
Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.ORCID iD: 0000-0003-4222-9631
2019 (English)In: Quality and Reliability Engineering International, ISSN 0748-8017, E-ISSN 1099-1638, Vol. 35, no 5, p. 1408-1423Article in journal (Refereed) Published
Abstract [en]

As a direct consequence of production systems' digitalization, high‐frequency and high‐dimensional data has become more easily available. In terms of data analysis, latent structures‐based methods are often employed when analyzing multivariate and complex data. However, these methods are designed for supervised learning problems when sufficient labeled data are available. Particularly for fast production rates, quality characteristics data tend to be scarcer than available process data generated through multiple sensors and automated data collection schemes. One way to overcome the problem of scarce outputs is to employ semi‐supervised learning methods, which use both labeled and unlabeled data. It has been shown that it is advantageous to use a semi‐supervised approach in case of labeled data and unlabeled data coming from the same distribution. In real applications, there is a chance that unlabeled data contain outliers or even a drift in the process, which will affect the performance of the semi‐supervised methods. The research question addressed in this work is how to detect outliers in the unlabeled data set using the scarce labeled data set. An iterative strategy is proposed using a combined Hotelling's T2 and Q statistics and applied using a semi‐supervised principal component regression (SS‐PCR) approach on both simulated and real data sets.

Place, publisher, year, edition, pages
John Wiley & Sons, 2019. Vol. 35, no 5, p. 1408-1423
Keywords [en]
Industry 4.0, iterative strategy, latent structures methods, production statistics, semi‐supervised learning
National Category
Reliability and Maintenance
Research subject
Quality technology and logistics
Identifiers
URN: urn:nbn:se:ltu:diva-75583DOI: 10.1002/qre.2522ISI: 000477441600001OAI: oai:DiVA.org:ltu-75583DiVA, id: diva2:1343720
Conference
18th Annual Conference of the European Network for Business and Industrial Statistic, 2-6 September 2018, Ecoles des Mines, Nancy, France.
Note

Konferensartikel i tidskrift

Available from: 2019-08-19 Created: 2019-08-19 Last updated: 2019-08-28Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Authority records BETA

Kulahci, Murat

Search in DiVA

By author/editor
Kulahci, Murat
By organisation
Business Administration and Industrial Engineering
In the same journal
Quality and Reliability Engineering International
Reliability and Maintenance

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 2 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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