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» Bagging, Boosting, and C4.5
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PAMI
2007
166views more  PAMI 2007»
13 years 7 months ago
A Comparison of Decision Tree Ensemble Creation Techniques
Abstract—We experimentally evaluate bagging and seven other randomizationbased approaches to creating an ensemble of decision tree classifiers. Statistical tests were performed o...
Robert E. Banfield, Lawrence O. Hall, Kevin W. Bow...
DIS
2010
Springer
13 years 6 months ago
Speeding Up and Boosting Diverse Density Learning
Abstract. In multi-instance learning, each example is described by a bag of instances instead of a single feature vector. In this paper, we revisit the idea of performing multi-ins...
James R. Foulds, Eibe Frank
AUSAI
2001
Springer
14 years 4 days ago
Wrapping Boosters against Noise
Abstract. Wrappers have recently been used to obtain parameter optimizations for learning algorithms. In this paper we investigate the use of a wrapper for estimating the correct n...
Bernhard Pfahringer, Geoffrey Holmes, Gabi Schmidb...
ECAI
2006
Springer
13 years 11 months ago
Ensembles of Grafted Trees
Grafted trees are trees that are constructed using two methods. The first method creates an initial tree, while the second method is used to complete the tree. In this work, the fi...
Juan José Rodríguez, Jesús Ma...
ANNPR
2006
Springer
13 years 11 months ago
Visual Classification of Images by Learning Geometric Appearances Through Boosting
We present a multiclass classification system for gray value images through boosting. The feature selection is done using the LPBoost algorithm which selects suitable features of a...
Martin Antenreiter, Christian Savu-Krohn, Peter Au...