The effect of class-balance and class-overlap in the training set for multivariate and product-adapted grading of Scots pine sawn timber

ABSTRACT

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.

KEYWORDS:

• Sawn timber
• PLS regression
• machine-learning
• training aspects

Acknowledgments

Financial support from the Swedish Innovation Agency (Vinnova), project Sawmill 4.0 – Customised flexible sawmill production by integrating data-driven models and decisions tools 2018-02749, is gratefully acknowledged. The authors also gratefully acknowledge the support of the CT-Wood – a centre of excellence at Luleå University of Technology for the development of X-ray computed tomography applications for use in the forest products industry.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

Financial support from the Swedish Innovation Agency (VINNOVA), project Sawmill 4.0 – Customised flexible sawmill production by integrating data-driven models and decisions tools 2018-02749, is gratefully acknowledged.