C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Ensemble learning via negative correlation
Neural Networks
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pose Invariant Face Recognition
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Classification of seismic signals by integrating ensembles ofneural networks
IEEE Transactions on Signal Processing
Making use of population information in evolutionary artificialneural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Stopping criteria for ensembles of evolutionary artificial neural networks
Design and application of hybrid intelligent systems
Ensemble Pruning Via Semi-definite Programming
The Journal of Machine Learning Research
A Two-Step Selective Region Ensemble for Facial Age Estimation
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Particle swarm optimization based multi-prototype ensembles
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Cooperative coevolutionary ensemble learning
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Voting-averaged combination method for regressor ensemble
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
Advances in Engineering Software
A novel classifier ensemble method based on class weightening in huge dataset
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
Aggregating regressive estimators: gradient-based neural network ensemble
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
Subnet weight modification algorithm for ensemble
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
HyperSurface classifiers ensemble for high dimensional data sets
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Customer churn prediction by hybrid model
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Genetically evolved trees representing ensembles
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
A heuristic diversity production approach
ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part III
A diversity production approach in ensemble of base classifiers
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
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Neural network ensemble is a learning paradigm where several neural networks are jointly used to solve a problem. In this paper, the relationship between the generalization ability of the neural network ensemble and the correlation of the individual neural networks is analyzed, which reveals that ensembling a selective subset of individual networks is superior to ensembling all the individual networks in some cases. Therefore an approach named GASEN is proposed, which trains several individual neural networks and then employs genetic algorithm to select an optimum subset of individual networks to constitute an ensemble. Experimental results show that, comparing with a popular ensemble approach, i.e. averaging all, and a theoretically optimum selective ensemble approach, i.e. enumerating, GASEN has preferable performance in generating ensembles with strong generalization ability in relatively small computational cost.