Statistical Methods for Classification of Wooden Boards
(2014) FMS820 20142Mathematical Statistics
- Abstract (Swedish)
- The quality inspection of wooden boards is experiencing a large change. By the use of
camera and laser technology board characteristics and defects can be instantly
identified and measured. This thesis investigates how the information from a quality
inspection system can be used to classify boards into different quality classes, by the
use of statistical classification models. Two types of classification models have been
tested, Logistic Regression and Support Vector Machines. To deal with potential
overfitting a regularized version of Logistic Regression is implemented, and to deal
with ordinal dependent variables a logistic regression model for ordinal variables has
been implemented. The classification models have been tested... (More) - The quality inspection of wooden boards is experiencing a large change. By the use of
camera and laser technology board characteristics and defects can be instantly
identified and measured. This thesis investigates how the information from a quality
inspection system can be used to classify boards into different quality classes, by the
use of statistical classification models. Two types of classification models have been
tested, Logistic Regression and Support Vector Machines. To deal with potential
overfitting a regularized version of Logistic Regression is implemented, and to deal
with ordinal dependent variables a logistic regression model for ordinal variables has
been implemented. The classification models have been tested against board strength
classes, and similar results have been obtained by most models. It is concluded that
the regularized logistic regression is the model that manages to classify most boards
correctly, but the Support Vector Machine produces a better result on classes where
training data is scarce.
The thesis was done on behalf of RemaSawco AB, a company that manufactures
measurement systems and inspection systems for the sawmill industry. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/4858231
- author
- Lindell, Johan
- supervisor
- organization
- course
- FMS820 20142
- year
- 2014
- type
- H2 - Master's Degree (Two Years)
- subject
- language
- English
- id
- 4858231
- date added to LUP
- 2014-12-09 08:03:05
- date last changed
- 2014-12-09 08:03:05
@misc{4858231, abstract = {{The quality inspection of wooden boards is experiencing a large change. By the use of camera and laser technology board characteristics and defects can be instantly identified and measured. This thesis investigates how the information from a quality inspection system can be used to classify boards into different quality classes, by the use of statistical classification models. Two types of classification models have been tested, Logistic Regression and Support Vector Machines. To deal with potential overfitting a regularized version of Logistic Regression is implemented, and to deal with ordinal dependent variables a logistic regression model for ordinal variables has been implemented. The classification models have been tested against board strength classes, and similar results have been obtained by most models. It is concluded that the regularized logistic regression is the model that manages to classify most boards correctly, but the Support Vector Machine produces a better result on classes where training data is scarce. The thesis was done on behalf of RemaSawco AB, a company that manufactures measurement systems and inspection systems for the sawmill industry.}}, author = {{Lindell, Johan}}, language = {{eng}}, note = {{Student Paper}}, title = {{Statistical Methods for Classification of Wooden Boards}}, year = {{2014}}, }