IEEE Transactions on Pattern Analysis and Machine Intelligence
A Theoretical Study on Six Classifier Fusion Strategies
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sum Versus Vote Fusion in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
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
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
Dynamic ensemble approach for estimating organic carbon using computational intelligence
ACST'06 Proceedings of the 2nd IASTED international conference on Advances in computer science and technology
Managing Diversity in Regression Ensembles
The Journal of Machine Learning Research
Error bounds of decision templates and support vector machines in decision fusion
International Journal of Knowledge Engineering and Soft Data Paradigms
Between two extremes: examining decompositions of the ensemble objective function
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
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In this paper, we continue the theoretical and experimental analysis of two widely used combining rules, namely, the simple and weighted average of classifier outputs, that we started in previous works. We analyse and compare the conditions which affect the performance improvement achievable by weighted average over simple average, and over individual classifiers, under the assumption of unbiased and uncorrelated estimation errors. Although our theoretical results have been obtained under strict assumptions, the reported experiments show that they can be useful in real applications, for designing multiple classifier systems based on linear combiners.