Neural networks and the bias/variance dilemma
Neural Computation
Methods for combining experts' probability assessments
Neural Computation
Machine Learning
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
Acoustic Emission, Cylinder Pressure and Vibration: A Multisensor Approach to Robust Fault Diagnosis
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
The Knowledge Engineering Review
Engineering multiversion neural-net systems
Neural Computation
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Ensembles of Learning Machines
WIRN VIETRI 2002 Proceedings of the 13th Italian Workshop on Neural Nets-Revised Papers
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
A Robust Multiple Classifier System for a Partially Unsupervised Updating of Land-Cover Maps
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Methods for Designing Multiple Classifier Systems
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Realization of a Virtual Lambda Sensor on a Fixed Precision System
Proceedings of the conference on Design, Automation and Test in Europe - Volume 3
Eigenclassifiers for combining correlated classifiers
Information Sciences: an International Journal
Making Diversity Enhancement Based on Multiple Classifier System by Weight Tuning
Neural Processing Letters
A double pruning algorithm for classification ensembles
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
An efficient ensemble classification method based on novel classifier selection technique
Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics
A fuzzy evolutionary framework for combining ensembles
Applied Soft Computing
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
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The performance of neural nets can be improved through the use of ensembles of redundant nets. In this paper, some of the available methods of ensemble creation are reviewed and the "test and select" methodolology for ensemble creation is considered. This approach involves testing potential ensemble combinations on a validation set, and selecting the best performing ensemble on this basis, which is then tested on a final test set. The application of this methodology, and of ensembles in general, is explored further in two case studies. The first case study is of fault diagnosis in a diesel engine, and relies on ensembles of nets trained from three different data sources. The second case study is of robot localisation, using an evidence-shifting method based on the output of trained SOMs. In both studies, improved results are obtained as a result of combining nets to form ensembles.