Abstract
We present an ensemble of classifiers that can be used to predict quality characteristics of an important process in pulp and paper industry: the tissue softness estimation. This classification problem is a difficult one since, with respect to our data set, the accuracy of all the well-known classifiers is below 68%. Contrary to that, the bagging random trees ensemble model is able to increase the accuracy up to 75%.
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© 2005 Springer-Verlag Berlin Heidelberg
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Kotsiantis, S.B., Tsekouras, G.E., Pintelas, P.E. (2005). Bagging Random Trees for Estimation of Tissue Softness. In: Perner, P., Imiya, A. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2005. Lecture Notes in Computer Science(), vol 3587. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11510888_67
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DOI: https://doi.org/10.1007/11510888_67
Publisher Name: Springer, Berlin, Heidelberg
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