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Machine Learning Algorithms in Heavy Process Manufacturing
Dalarna University, School of Technology and Business Studies, Computer Engineering.
Dalarna University, School of Technology and Business Studies, Computer Engineering.
Dalarna University, School of Technology and Business Studies, Computer Engineering.
Dalarna University, School of Technology and Business Studies, Computer Engineering.ORCID iD: 0000-0002-1429-2345
2016 (English)In: American Journal of Intelligent Systems, ISSN 2165-8978, E-ISSN 2165-8994, Vol. 6, no 1, 1-13 p.Article in journal (Refereed) 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.

Place, publisher, year, edition, pages
2016. Vol. 6, no 1, 1-13 p.
Keyword [en]
Heavy Process Manufacturing, Machine Learning, SVM, MLP, DT, RF, Feature Selection, Calibration
National Category
Signal Processing
Research subject
Complex Systems – Microdata Analysis
Identifiers
URN: urn:nbn:se:du-21490OAI: oai:DiVA.org:du-21490DiVA: diva2:931020
Available from: 2016-05-26 Created: 2016-05-26 Last updated: 2017-11-30Bibliographically approved

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