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ICMLA
2008
13 years 10 months ago
Decision Tree Ensemble: Small Heterogeneous Is Better Than Large Homogeneous
Using decision trees that split on randomly selected attributes is one way to increase the diversity within an ensemble of decision trees. Another approach increases diversity by ...
Michael Gashler, Christophe G. Giraud-Carrier, Ton...
BMCBI
2005
100views more  BMCBI 2005»
13 years 8 months ago
EvDTree: structure-dependent substitution profiles based on decision tree classification of 3D environments
Background: Structure-dependent substitution matrices increase the accuracy of sequence alignments when the 3D structure of one sequence is known, and are successful e.g. in fold ...
Jean-Christophe Gelly, Laurent Chiche, Jér&...
PAMI
2007
166views more  PAMI 2007»
13 years 8 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...
BMCBI
2008
219views more  BMCBI 2008»
13 years 8 months ago
Classification of premalignant pancreatic cancer mass-spectrometry data using decision tree ensembles
Background: Pancreatic cancer is the fourth leading cause of cancer death in the United States. Consequently, identification of clinically relevant biomarkers for the early detect...
Guangtao Ge, G. William Wong
ICML
2009
IEEE
14 years 3 months ago
Decision tree and instance-based learning for label ranking
The label ranking problem consists of learning a model that maps instances to total orders over a finite set of predefined labels. This paper introduces new methods for label ra...
Weiwei Cheng, Jens C. Huhn, Eyke Hüllermeier