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ICMLA
2008
13 years 5 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...
ICDM
2009
IEEE
124views Data Mining» more  ICDM 2009»
13 years 10 months ago
Rule Ensembles for Multi-target Regression
—Methods for learning decision rules are being successfully applied to many problem domains, especially where understanding and interpretation of the learned model is necessary. ...
Timo Aho, Bernard Zenko, Saso Dzeroski
KDD
2001
ACM
216views Data Mining» more  KDD 2001»
14 years 4 months ago
The distributed boosting algorithm
In this paper, we propose a general framework for distributed boosting intended for efficient integrating specialized classifiers learned over very large and distributed homogeneo...
Aleksandar Lazarevic, Zoran Obradovic
BIOID
2008
103views Biometrics» more  BIOID 2008»
13 years 5 months ago
Promoting Diversity in Gaussian Mixture Ensembles: An Application to Signature Verification
Abstract. Classifiers based on Gaussian mixture models are good performers in many pattern recognition tasks. Unlike decision trees, they can be described as stable classifier: a s...
Jonas Richiardi, Andrzej Drygajlo, Laetitia Todesc...
BIBE
2005
IEEE
13 years 9 months ago
Diagnostic Rules Induced by an Ensemble Method for Childhood Leukemia
We introduce a new ensemble method based on decision tree to discover significant and diversified rules for subtype classification of childhood acute lymphoblastic leukemia, a ...
Jinyan Li, Huiqing Liu, Ling Li