The Strength of Weak Learnability
Machine Learning
Original Contribution: Stacked generalization
Neural Networks
Combining the results of several neural network classifiers
Neural Networks
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Machine Learning
Optimal linear combinations of neural networks
Neural Networks
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
MultiBoosting: A Technique for Combining Boosting and Wagging
Machine Learning
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
Improving Regressors using Boosting Techniques
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Adaptive mixtures of local experts
Neural Computation
Computational methods for a mathematical theory of evidence
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
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The use of ensemble models in many problem domains has increased significantly in the last fewyears. The ensemble modeling, in particularly boosting, has shown a great promise in improving predictive performance of a model. Combining the ensemble members is normally done in a co-operative fashion where each of the ensemble members performs the same task and their predictions are aggregated to obtain the improved performance. However, it is also possible to combine the ensemble members in a competitive fashion where the best prediction of a relevant ensemble member is selected for a particular input. This option has been previously somewhat overlooked. The aim of this article is to investigate and compare the competitive and co-operative approaches to combining the models in the ensemble. A comparison is made between a competitive ensemble model and that of MARS with bagging, mixture of experts, hierarchical mixture of experts and a neural network ensemble over several public domain regression problems that have a high degree of nonlinearity and noise. The empirical results showa substantial advantage of competitive learning versus the co-operative learning for all the regression problems investigated. The requirements for creating the efficient ensembles and the available guidelines are also discussed.