du.sePublikasjoner
Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • 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
Machine Learning Algorithms in Heavy Process Manufacturing
Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.ORCID-id: 0000-0002-1429-2345
2016 (engelsk)Inngår i: American Journal of Intelligent Systems, ISSN 2165-8978, E-ISSN 2165-8994, Vol. 6, nr 1, s. 1-13Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

In a global economy, manufacturers mainly compete with cost efficiency of production, as the price of raw materials are similar worldwide. Heavy industry has two big issues to deal with. On the one hand there is lots of data which needs to be analyzed in an effective manner, and on the other hand making big improvements via investments in cooperate structure or new machinery is neither economically nor physically viable. Machine learning offers a promising way for manufacturers to address both these problems as they are in an excellent position to employ learning techniques with their massive resource of historical production data. However, choosing modelling a strategy in this setting is far from trivial and this is the objective of this article. The article investigates characteristics of the most popular classifiers used in industry today. Support Vector Machines, Multilayer Perceptron, Decision Trees, Random Forests, and the meta-algorithms Bagging and Boosting are mainly investigated in this work. Lessons from real-world implementations of these learners are also provided together with future directions when different learners are expected to perform well. The importance of feature selection and relevant selection methods in an industrial setting are further investigated. Performance metrics have also been discussed for the sake of completion.

sted, utgiver, år, opplag, sider
2016. Vol. 6, nr 1, s. 1-13
Emneord [en]
Heavy Process Manufacturing, Machine Learning, SVM, MLP, DT, RF, Feature Selection, Calibration
HSV kategori
Forskningsprogram
Komplexa system - mikrodataanalys
Identifikatorer
URN: urn:nbn:se:du-21490DOI: 10.5923/j.ajis.20160601.01OAI: oai:DiVA.org:du-21490DiVA, id: diva2:931020
Tilgjengelig fra: 2016-05-26 Laget: 2016-05-26 Sist oppdatert: 2019-10-17bibliografisk kontrollert

Open Access i DiVA

fulltext(524 kB)446 nedlastinger
Filinformasjon
Fil FULLTEXT02.pdfFilstørrelse 524 kBChecksum SHA-512
e363c3047f25569cb5a8a0fd2588d02531d3c5aeb8534cd3dfafef4f5f6cc504ad942aa3f318b087c589bed45b86e199b99497e446961544b581f396ef9fcf95
Type fulltextMimetype application/pdf

Andre lenker

Forlagets fulltekst

Personposter BETA

Hansson, KarlYella, SirilDougherty, MarkFleyeh, Hasan

Søk i DiVA

Av forfatter/redaktør
Hansson, KarlYella, SirilDougherty, MarkFleyeh, Hasan
Av organisasjonen
I samme tidsskrift
American Journal of Intelligent Systems

Søk utenfor DiVA

GoogleGoogle Scholar
Totalt: 536 nedlastinger
Antall nedlastinger er summen av alle nedlastinger av alle fulltekster. Det kan for eksempel være tidligere versjoner som er ikke lenger tilgjengelige

doi
urn-nbn

Altmetric

doi
urn-nbn
Totalt: 1546 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • 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