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The effect of class...
The effect of class-balance and class-overlap in the training set for multivariate and product-adapted grading of Scots pine sawn timber
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- Olofsson, Linus (författare)
- Luleå tekniska universitet,Träteknik
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- Broman, Olof (författare)
- Luleå tekniska universitet,Träteknik
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- Oja, Johan (författare)
- Luleå tekniska universitet,Träteknik
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- Sandberg, Dick, 1967- (författare)
- Luleå tekniska universitet,Träteknik
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visa färre...
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(creator_code:org_t)
- 2020-09-04
- 2021
- Engelska.
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Ingår i: Wood Material Science & Engineering. - London : Taylor & Francis Group. - 1748-0272 .- 1748-0280. ; 16:1, s. 58-63
- Relaterad länk:
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https://doi.org/10.1...
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https://ltu.diva-por... (primary) (Raw object)
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https://www.tandfonl...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Using multivariate partial least squares regression (PLS) to perform visual quality grading of sawn timber requires a training set with known quality grades for the training of a grading model. This study evaluated the grading accuracy of an independent test set of sawn timber when changing the aspects of class-balance and class-overlap of the training set consisting of 251 planks. The study also compared two ways of expressing the reference-grade of the training set; by grading images picturing the planks, and by grading the product produced from the planks. Two grading models were trained using each reference-grade to establish a baseline for comparison. Both models achieved a 76% grading accuracy of the test set, indicating that both reference-grades can be used to train comparable models. To study the class-balance and class-overlap aspects of the training set, 25% of the training set was removed in two training scenarios. The models trained on class-balanced data indicated that class-imbalance of the training set was not a problem. The models trained on data with less class-overlap using the product-grade reference suffered a 4%-points grading accuracy loss due to the smaller training set, while the model trained using the image-grade reference retained its grading accuracy.
Ämnesord
- LANTBRUKSVETENSKAPER -- Lantbruksvetenskap, skogsbruk och fiske -- Trävetenskap (hsv//swe)
- AGRICULTURAL SCIENCES -- Agriculture, Forestry and Fisheries -- Wood Science (hsv//eng)
Nyckelord
- Sawn timber
- PLS regression
- machine-learning
- training aspects
- Träteknik
- Wood Science and Engineering
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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