Bagging random trees for estimation of tissue softness

Authors:
S. B. Kotsiantis;G. E. Tsekouras;P. E. Pintelas
Affiliations:
Educational Software Development Laboratory, Department of Mathematics, University of Patras, Greece;Department of Cultural Technology and Communication, University of the Aegean, Mytilene, Greece;Educational Software Development Laboratory, Department of Mathematics, University of Patras, Greece
Venue:
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
Year:
2005

Citing 15
Cited 0

C4.5: programs for machine learning

C4.5: programs for machine learning
Bagging predictors

Machine Learning
Lazy learning

Lazy learning
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss

Machine Learning - Special issue on learning with probabilistic representations
Separate-and-Conquer Rule Learning

Artificial Intelligence Review
Data mining: practical machine learning tools and techniques with Java implementations

Data mining: practical machine learning tools and techniques with Java implementations
Using analytic QP and sparseness to speed training of support vector machines

Proceedings of the 1998 conference on Advances in neural information processing systems II
MultiBoosting: A Technique for Combining Boosting and Wagging

Machine Learning
Machine Learning

Machine Learning
Random Forests

Machine Learning
A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey

Data Mining and Knowledge Discovery
An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants

Machine Learning
Inference for the Generalization Error

Machine Learning
Constructing diverse classifier ensembles using artificial training examples

IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence

Quantified Score

Hi-index	0.00

Visualization

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%.