Original Contribution: Stacked generalization
Neural Networks
Improving regression estimation: Averaging methods for variance reduction with extensions to general convex measure optimization
Machine Learning
Optimal linear combinations of neural networks
Neural Networks
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminative metric design for robust pattern recognition
IEEE Transactions on Signal Processing
Parallel consensual neural networks
IEEE Transactions on Neural Networks
On Fusers that Perform Better than Best Sensor
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ensembling neural networks: many could be better than all
Artificial Intelligence
Lithology Recognition by Neural Network Ensembles
SBIA '02 Proceedings of the 16th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
Error Rejection in Linearly Combined Multiple Classifiers
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Analysis of Linear and Order Statistics Combiners for Fusion of Imbalanced Classifiers
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Performance Analysis and Comparison of Linear Combiners for Classifier Fusion
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Extracting symbolic rules from trained neural network ensembles
AI Communications - Special issue on Artificial intelligence advances in China
A Theoretical and Experimental Analysis of Linear Combiners for Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Effective classification of noisy data streams with attribute-oriented dynamic classifier selection
Knowledge and Information Systems
Extracting symbolic rules from trained neural network ensembles
AI Communications - Artificial Intelligence Advances in China
Adaptive fusion and co-operative training for classifier ensembles
Pattern Recognition
Trainable fusion rules. I. Large sample size case
Neural Networks
Calligraphic Interfaces: Classifier combination for sketch-based 3D part retrieval
Computers and Graphics
Classifier ensemble selection using hybrid genetic algorithms
Pattern Recognition Letters
Estimation and decision fusion: A survey
Neurocomputing
Increasing classification efficiency with multiple mirror classifiers
Expert Systems with Applications: An International Journal
Resampling-based selective clustering ensembles
Pattern Recognition Letters
A neural-network-based nonlinear metamodeling approach to financial time series forecasting
Applied Soft Computing
Incremental construction of classifier and discriminant ensembles
Information Sciences: an International Journal
Data dependency in multiple classifier systems
Pattern Recognition
Cluster Ensembles Based on Vector Space Embeddings of Graphs
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
A boundary based classifier combination method
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Classifier combination based on confidence transformation
Pattern Recognition
Combining fingerprint and hand-geometry verification decisions
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Data dependence in combining classifiers
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Neural net ensembles for lithology recognition
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
A PSO-based weighting method for linear combination of neural networks
Computers and Electrical Engineering
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
Partial AUC maximization in a linear combination of dichotomizers
Pattern Recognition
A probability-based unified 3d shape search
SAMT'06 Proceedings of the First international conference on Semantic and Digital Media Technologies
Recognition of complex human behaviors in pool environment using foreground silhouette
ISVC'05 Proceedings of the First international conference on Advances in Visual Computing
Decoding rules for error correcting output code ensembles
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Classifiers selection in ensembles using genetic algorithms for bankruptcy prediction
Expert Systems with Applications: An International Journal
Sketch-based 3D engineering part class browsing and retrieval
SBM'06 Proceedings of the Third Eurographics conference on Sketch-Based Interfaces and Modeling
Reduced analytical dependency modeling for classifier fusion
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Linear classifier combination and selection using group sparse regularization and hinge loss
Pattern Recognition Letters
Multiscale convolutional neural networks for vision: based classification of cells
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Proceedings of the 3rd ACM international workshop on Audio/visual emotion challenge
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With a focus on classification problems, this paper presents a new method for linearly combining multiple neural network classifiers based on statistical pattern recognition theory. In our approach, several neural networks are first selected based on which works best for each class in terms of minimizing classification errors. Then, they are linearly combined to form an ideal classifier that exploits the strengths of the individual classifiers. In this approach, the minimum classification error (MCE) criterion is utilized to estimate the optimal linear weights. In this formulation, because the classification decision rule is incorporated into the cost function, a more suitable better combination of weights for the classification objective could be obtained. Experimental results using artificial and real data sets show that the proposed method can construct a better combined classifier that outperforms the best single classifier in terms of overall classification errors for test data.