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PAMI
1998
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13 years 4 months ago
The Random Subspace Method for Constructing Decision Forests
—Much of previous attention on decision trees focuses on the splitting criteria and optimization of tree sizes. The dilemma between overfitting and achieving maximum accuracy is ...
Tin Kam Ho
ECML
2003
Springer
13 years 9 months ago
Improving the AUC of Probabilistic Estimation Trees
Abstract. In this work we investigate several issues in order to improve the performance of probabilistic estimation trees (PETs). First, we derive a new probability smoothing that...
César Ferri, Peter A. Flach, José He...
SAC
2004
ACM
13 years 10 months ago
Forest trees for on-line data
This paper presents an hybrid adaptive system for induction of forest of trees from data streams. The Ultra Fast Forest Tree system (UFFT) is an incremental algorithm, with consta...
João Gama, Pedro Medas, Ricardo Rocha
SAC
2005
ACM
13 years 10 months ago
Learning decision trees from dynamic data streams
: This paper presents a system for induction of forest of functional trees from data streams able to detect concept drift. The Ultra Fast Forest of Trees (UFFT) is an incremental a...
João Gama, Pedro Medas, Pedro Pereira Rodri...
ICML
2000
IEEE
14 years 5 months ago
Exploiting the Cost (In)sensitivity of Decision Tree Splitting Criteria
This paper investigates how the splitting criteria and pruning methods of decision tree learning algorithms are influenced by misclassification costs or changes to the class distr...
Chris Drummond, Robert C. Holte