Neural Networks
Original Contribution: Stacked generalization
Neural Networks
Machine Learning
Machine Learning
Neural Network Methodology for 1H NMR Spectroscopy Classification
ICIIS '99 Proceedings of the 1999 International Conference on Information Intelligence and Systems
A note on Platt's probabilistic outputs for support vector machines
Machine Learning
Overfitting in the selection of classifier ensembles: a comparative study between PSO and GA
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Automatic Classification of NMR Spectra by Ensembles of Local Experts
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Issues in stacked generalization
Journal of Artificial Intelligence Research
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Margin optimization based pruning for random forest
Neurocomputing
Selective ensemble of support vector data descriptions for novelty detection
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
Diversity regularized ensemble pruning
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
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An ensemble is generated by training multiple component learners for a same task and then combining them for predictions. It is known that when lots of trained learners are available, it is better to ensemble some instead of all of them. The selection, however, is generally difficult and heuristics are often used. In this paper, we investigate the problem under the regularization framework, and propose a regularized selective ensemble algorithm RSE. In RSE, the selection is reduced to a quadratic programming problem, which has a sparse solution and can be solved efficiently. Since it naturally fits the semi-supervised learning setting, RSE can also exploit unlabeled data to improve the performance. Experimental results show that RSE can generate ensembles with small size but strong generalization ability.