A Method of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals
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
Proceedings of the Second International Workshop on Multiple Classifier Systems
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Linear and order statistics combiners for reliable pattern classification
Linear and order statistics combiners for reliable pattern classification
Application of majority voting to pattern recognition: an analysis of its behavior and performance
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Analysis of Linear and Order Statistics Combiners for Fusion of Imbalanced Classifiers
MCS '02 Proceedings of the Third 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
Classifier combination schemes in speech impediment therapy systems
Acta Cybernetica
Using diversity of errors for selecting members of a committee classifier
Pattern Recognition
Combining Methods for Dynamic Multiple Classifier Systems
ANNPR '08 Proceedings of the 3rd IAPR workshop on Artificial Neural Networks in Pattern Recognition
Semi-supervised Co-update of Multiple Matchers
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Classifier combination based on confidence transformation
Pattern Recognition
Linear combiners for classifier fusion: some theoretical and experimental results
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Comparison of classifier selection methods for improving committee performance
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
Using independence assumption to improve multimodal biometric fusion
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
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In this paper, we report a theoretical and experimental comparison between two widely used combination rules for classifier fusion: simple average and weighted average of classifiers outputs. We analyse the conditions which affect the difference between the performance of simple and weighted averaging and discuss the relation between these conditions and the concept of classifiers' "imbalance". Experiments aimed at assessing some of the theoretical results for cases where the theoretical assumptions could not be hold are reported.