Ensembling neural networks: many could be better than all
Artificial Intelligence
A Mixture-of-Experts Framework for Learning from Imbalanced Data Sets
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Extracting symbolic rules from trained neural network ensembles
AI Communications - Special issue on Artificial intelligence advances in China
3D seismic volume visualization
Integrated image and graphics technologies
A mixture-of-experts framework for text classification
ConLL '01 Proceedings of the 2001 workshop on Computational Natural Language Learning - Volume 7
Extracting symbolic rules from trained neural network ensembles
AI Communications - Artificial Intelligence Advances in China
Segmental Hidden Markov Models with Random Effects for Waveform Modeling
The Journal of Machine Learning Research
Principles for the Development of Upper Ontologies in Higher-level Information Fusion Applications
Proceedings of the 2006 conference on Formal Ontology in Information Systems: Proceedings of the Fourth International Conference (FOIS 2006)
Genetic algorithm based selective neural network ensemble
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Neural network classifers in arrears management
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Modeling meteorological prediction using particle swarm optimization and neural network ensemble
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
CCC: classifier combination via classifier
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing
Lung cancer cell identification based on artificial neural network ensembles
Artificial Intelligence in Medicine
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part II
Computers in Biology and Medicine
An ensemble of computational intelligence models for software maintenance effort prediction
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
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We examine a classification problem in which seismic waveforms of natural earthquakes are to be distinguished from waveforms of man-made explosions. We present an integrated classification machine (ICM), which is a hierarchy of artificial neural networks (ANNs) that are trained to classify the seismic waveforms. In order to maximize the gain of combining the multiple ANNs, we suggest construction of a redundant classification environment (RCE) that consists of several “experts” whose expertise depends on the different input representations to which they are exposed. In the proposed scheme, the experts are ensembles of ANN, trained on different bootstrap replicas. We use various network architectures, different time-frequency decompositions of the seismic waveforms, and various smoothing levels in order to achieve an RCE. A confidence measure for the ensemble's classification is defined based on the agreement (variance) within the ensembles, and an algorithm for a nonlinear integration of the ensembles using this measure is presented. An implementation on a data set of 380 seismic events is described, where the proposed ICM had classified correctly 92% of the testing signals. The comparison we made with classical methods indicates that combining a collection of ensembles of ANNs can be used to handle complex high dimensional classification problems