Original Contribution: Stacked generalization
Neural Networks
Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
A Method of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal combinations of pattern classifiers
Pattern Recognition Letters
Fusion of handwritten word classifiers
Pattern Recognition Letters - Special issue on fuzzy set technology in pattern recognition
Combination of Multiple Classifiers Using Local Accuracy Estimates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal linear combinations of neural networks
Neural Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Soft combination of neural classifiers: a comparative study
Pattern Recognition Letters
Optimal Linear Combination of Neural Networks for Improving Classification Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
An approach to the automatic design of multiple classifier systems
Pattern Recognition Letters - Special issue on machine learning and data mining in pattern recognition
A Theoretical Study on Six Classifier Fusion Strategies
IEEE Transactions on Pattern Analysis and Machine Intelligence
On combining classifiers using sum and product rules
Pattern Recognition Letters
Modular Neural Network Classifiers: A Comparative Study
Journal of Intelligent and Robotic Systems
Proceedings of the Third International Workshop on Multiple Classifier Systems
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Trainable Multiple Classifier Schemes for Handwritten Character Recognition
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Switching between selection and fusion in combining classifiers: anexperiment
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Adaptive fusion and co-operative training for classifier ensembles
Pattern Recognition
Classifier Ensembles with a Random Linear Oracle
IEEE Transactions on Knowledge and Data Engineering
Cost-sensitive boosting for classification of imbalanced data
Pattern Recognition
Data dependency in multiple classifier systems
Pattern Recognition
Computational Statistics & Data Analysis
Classifiers fusion for EEG signals processing in human-computer interface systems
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Empirical study on weighted voting multiple classifiers
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
Data partitioning evaluation measures for classifier ensembles
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
The use of artificial-intelligence-based ensembles for intrusion detection: a review
Applied Computational Intelligence and Soft Computing
A review of information fusion techniques employed in iris recognition systems
International Journal of Advanced Intelligence Paradigms
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It has been accepted that multiple classifier systems provide a platform for not only performance improvement, but more efficient and robust pattern classification systems. A variety of combining methods have been proposed in the literature and some work has focused on comparing and categorizing these approaches. In this paper we present a new categorization of these combining schemes based on their dependence on the data patterns being classified. Combining methods can be totally independent from the data, or they can be implicitly or explicitly dependent on the data. It is argued that data dependent, and especially explicitly data dependent, approaches represent the highest potential for improved performance. On the basis of this categorization, an architecture for explicit data dependent combining methods is discussed. Experimental results to illustrate the comparative performance of some combining methods according to this categorization is included.