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Predictive Ensemble Modelling - Experimental Comparison of Boosting Implementation Methods
V.F. Adegoke
, D. Chen
, S. Banissi
, E. Banissi
Graduate School
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Dive into the research topics of 'Predictive Ensemble Modelling - Experimental Comparison of Boosting Implementation Methods'. Together they form a unique fingerprint.
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Computer Science
Adaboost
100%
Base Classifier
100%
Ensemble Modeling
100%
Ensemble Technique
100%
Fold Cross Validation
100%
Neural Network
100%
Performance Metric
100%
Random Decision Forest
100%
Support Vector Machine
100%
Validation Method
100%
Keyphrases
10-fold Cross Validation
50%
AdaBoost
50%
Base Classifier
50%
Better Performance
50%
Boosting Method
50%
Cross-validation Method
50%
Decision Stump
50%
Ensemble Boosting
50%
Ensemble Methods
50%
Ensemble Model
100%
Implementation Method
100%
Neural Network
50%
Performance Metrics
50%
Random Forest
50%
Resampling Methods
50%
Support Vector Machine
50%
Mathematics
Cross-Validation
100%
Ensemble Model
100%
Neural Network
100%
Resampling Method
100%
Support Vector Machine
100%