Boosting a weak learning algorithm by majority
Information and Computation
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Improved boosting algorithms using confidence-rated predictions
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Ensembling neural networks: many could be better than all
Artificial Intelligence
Improving Regressors using Boosting Techniques
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Ensemble Pruning Via Semi-definite Programming
The Journal of Machine Learning Research
Genetic algorithm based selective neural network ensemble
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Knowledge-Based Systems
Ensemble pruning using reinforcement learning
SETN'06 Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence
Selective SVMs ensemble driven by immune clonal algorithm
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
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A voting-averaged (VOA) method is presented to combine an ensemble for the regression tasks. VOA can select ensemble components dynamically using the hidden selectivity mechanism of voting, and hence VOA can be regarded as an improvement and extension of both voting and average methods. The experiment results of ten regression tasks show VOA and a representative selective average (SEA) method of GASEN (genetic algorithm-based selective ensemble), are of similar performances to each other, and both of better performance than simple average (SIA) in Bagging ensemble. SEA produces the ensemble subset in the using genetic optimization with validation datasets after the individuals are trained well; however, VOA combines a selective ensemble directly according to the cluster of the component outputs, not to determine ensemble subsets beforehand.