Optimal Linear Combination of Neural Networks for Improving Classification Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ensembles of Learning Machines
WIRN VIETRI 2002 Proceedings of the 13th Italian Workshop on Neural Nets-Revised Papers
Combining Parametric and Nonparametric Classifiers for an Unsupervised Updating of Land-Cover Maps
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Consensus Based Classification of Multisource Remote Sensing Data
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
Boosting, Bagging, and Consensus Based Classification of Multisource Remote Sensing Data
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
A Theoretical and Experimental Analysis of Linear Combiners for Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Calligraphic Interfaces: Classifier combination for sketch-based 3D part retrieval
Computers and Graphics
Soft computing system for bank performance prediction
Applied Soft Computing
Software reliability prediction by soft computing techniques
Journal of Systems and Software
Ensemble of support vector machines for land cover classification
International Journal of Remote Sensing
Failure prediction of dotcom companies using hybrid intelligent techniques
Expert Systems with Applications: An International Journal
A neural-network-based nonlinear metamodeling approach to financial time series forecasting
Applied Soft Computing
Border feature detection and adaptation algorithm for consensual decision making
International Journal of Remote Sensing - Remote Sensing: its Applications and Integration with GIS
Sparse ensembles using weighted combination methods based on linear programming
Pattern Recognition
A reliability-based RBF network ensemble model for foreign exchange rates predication
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Data fusion for fault diagnosis using dempster-shafer theory based multi-class SVMs
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
A probability-based unified 3d shape search
SAMT'06 Proceedings of the First international conference on Semantic and Digital Media Technologies
Multistage neural network metalearning with application to foreign exchange rates forecasting
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
A novel nonlinear neural network ensemble model for financial time series forecasting
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part I
Sketch-based 3D engineering part class browsing and retrieval
SBM'06 Proceedings of the Third Eurographics conference on Sketch-Based Interfaces and Modeling
Hybrid intelligent systems for predicting software reliability
Applied Soft Computing
International Journal of Applied Evolutionary Computation
A framework for selection and fusion of pattern classifiers in multimedia recognition
Pattern Recognition Letters
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A new type of a neural-network architecture, the parallel consensual neural network (PCNN), is introduced and applied in classification/data fusion of multisource remote sensing and geographic data. The PCNN architecture is based on statistical consensus theory and involves using stage neural networks with transformed input data. The input data are transformed several times and the different transformed data are used as if they were independent inputs. The independent inputs are first classified using the stage neural networks. The output responses from the stage networks are then weighted and combined to make a consensual decision. In this paper, optimization methods are used in order to weight the outputs from the stage networks. Two approaches are proposed to compute the data transforms for the PCNN, one for binary data and another for analog data. The analog approach uses wavelet packets. The experimental results obtained with the proposed approach show that the PCNN outperforms both a conjugate-gradient backpropagation neural network and conventional statistical methods in terms of overall classification accuracy of test data