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» Ensemble Approach for the Classification of Imbalanced Data
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DAWAK
2008
Springer
13 years 9 months ago
A Parameter-Free Associative Classification Method
In many application domains, classification tasks have to tackle multiclass imbalanced training sets. We have been looking for a CBA approach (Classification Based on Association r...
Loïc Cerf, Dominique Gay, Nazha Selmaoui, Jea...
SDM
2008
SIAM
177views Data Mining» more  SDM 2008»
13 years 9 months ago
Roughly Balanced Bagging for Imbalanced Data
Imbalanced class problems appear in many real applications of classification learning. We propose a novel sampling method to improve bagging for data sets with skewed class distri...
Shohei Hido, Hisashi Kashima
KDD
2003
ACM
148views Data Mining» more  KDD 2003»
14 years 7 months ago
Mining concept-drifting data streams using ensemble classifiers
Recently, mining data streams with concept drifts for actionable insights has become an important and challenging task for a wide range of applications including credit card fraud...
Haixun Wang, Wei Fan, Philip S. Yu, Jiawei Han
GECCO
2006
Springer
180views Optimization» more  GECCO 2006»
13 years 11 months ago
Improving cooperative GP ensemble with clustering and pruning for pattern classification
A boosting algorithm based on cellular genetic programming to build an ensemble of predictors is proposed. The method evolves a population of trees for a fixed number of rounds an...
Gianluigi Folino, Clara Pizzuti, Giandomenico Spez...
KDD
2002
ACM
157views Data Mining» more  KDD 2002»
14 years 7 months ago
Exploiting unlabeled data in ensemble methods
An adaptive semi-supervised ensemble method, ASSEMBLE, is proposed that constructs classification ensembles based on both labeled and unlabeled data. ASSEMBLE alternates between a...
Kristin P. Bennett, Ayhan Demiriz, Richard Maclin