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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 tekniska universitet, Institutionen för ekonomi, teknik och samhälle, Industriell Ekonomi.ORCID-id: 0000-0003-4222-9631
2019 (Engelska)Ingår i: Quality and Reliability Engineering International, ISSN 0748-8017, E-ISSN 1099-1638, Vol. 35, nr 5, s. 1408-1423Artikel i tidskrift (Refereegranskat) 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.

Ort, förlag, år, upplaga, sidor
John Wiley & Sons, 2019. Vol. 35, nr 5, s. 1408-1423
Nyckelord [en]
Industry 4.0, iterative strategy, latent structures methods, production statistics, semi‐supervised learning
Nationell ämneskategori
Tillförlitlighets- och kvalitetsteknik
Forskningsämne
Kvalitetsteknik och logistik
Identifikatorer
URN: urn:nbn:se:ltu:diva-75583DOI: 10.1002/qre.2522ISI: 000477441600001OAI: oai:DiVA.org:ltu-75583DiVA, id: diva2:1343720
Konferens
18th Annual Conference of the European Network for Business and Industrial Statistic, 2-6 September 2018, Ecoles des Mines, Nancy, France.
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Tillgänglig från: 2019-08-19 Skapad: 2019-08-19 Senast uppdaterad: 2019-08-28Bibliografiskt granskad

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Kulahci, Murat

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