Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Method of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals
IEEE Transactions on Pattern Analysis and Machine Intelligence
The nature of statistical learning theory
The nature of statistical learning theory
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
Optimal Linear Combination of Neural Networks for Improving Classification Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical and neural classifiers: an integrated approach to design
Statistical and neural classifiers: an integrated approach to design
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
An Experimental Comparison of Fixed and Trained Fusion Rules for Crisp Classifier Outputs
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Multiple Classification Systems in the Context of Feature Extraction and Selection
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Reduction of the Boasting Bias of Linear Experts
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Multiclassifier Systems: Back to the Future
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Experts' Boasting in Trainable Fusion Rules
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining multiple classifiers based on third-order dependency for handwritten numeral recognition
Pattern Recognition Letters
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
A Theoretical and Experimental Analysis of Linear Combiners for Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
A constructive algorithm for training cooperative neural network ensembles
IEEE Transactions on Neural Networks
Evolution of Multi-class Single Layer Perceptron
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
Multi-agent System Approach to React to Sudden Environmental Changes
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Incremental construction of classifier and discriminant ensembles
Information Sciences: an International Journal
Unsupervised Hierarchical Weighted Multi-segmenter
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Combining Multiple Classifiers with Dynamic Weighted Voting
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Some Remarks on Chosen Methods of Classifier Fusion Based on Weighted Voting
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Classifiers fusion in recognition of wheat varieties
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Multiple classifier methods for offline handwritten text line recognition
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Neuro-fuzzy-combiner: an effective multiple classifier system
International Journal of Knowledge Engineering and Soft Data Paradigms
Efficiency of local models ensembles for time series prediction
Expert Systems with Applications: An International Journal
An experimental study of one- and two-level classifier fusion for different sample sizes
Pattern Recognition Letters
A survey of multiple classifier systems as hybrid systems
Information Fusion
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A wide selection of standard statistical pattern classification algorithms can be applied as trainable fusion rules while designing neural network ensembles. A focus of the present two-part paper is finite sample effects: the complexity of base classifiers and fusion rules; the type of outputs provided by experts to the fusion rule; non-linearity of the fusion rule; degradation of experts and the fusion rule due to the lack of information in the design set; the adaptation of base classifiers to training set size, etc. In the first part of this paper, we consider arguments for utilizing continuous outputs of base classifiers versus categorical outputs and conclude: if one succeeds in having a small number of expert networks working perfectly in different parts of the input feature space, then crisp outputs may be preferable over continuous outputs. Afterwards, we oppose fixed fusion rules versus trainable ones and demonstrate situations where weighted average fusion can outperform simple average fusion. We present a review of statistical classification rules, paying special attention to these linear and non-linear rules, which are employed rarely but, according to our opinion, could be useful in neural network ensembles. We consider ideal and sample-based oracle decision rules and illustrate characteristic features of diverse fusion rules by considering an artificial two-dimensional (2D) example where the base classifiers perform well in different regions of input feature space.