A Method of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
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
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
A Theoretical and Experimental Analysis of Linear Combiners for Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Clustering data manipulation methods for the development of local specialists
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
Classifier ensembles: Select real-world applications
Information Fusion
Selection and Fusion of Similarity Measure Based Classifiers Using Support Vector Machines
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Reducing the overconfidence of base classifiers when combining their decisions
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Linear combiners for classifier fusion: some theoretical and experimental results
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
A study of ensemble of hybrid networks with strong regularization
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
A modular multiple classifier system for the detection of intrusions in computer networks
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Fusion of PCA-based and LDA-based similarity measures for face verification
EURASIP Journal on Advances in Signal Processing - Special issue on advanced image processing for defense and security applications
Multi-domain sentiment classification with classifier combination
Journal of Computer Science and Technology - Special issue on natural language processing
An experimental study of one- and two-level classifier fusion for different sample sizes
Pattern Recognition Letters
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Bimodal speaker identification using dynamic bayesian network
SINOBIOMETRICS'04 Proceedings of the 5th Chinese conference on Advances in Biometric Person Authentication
Reducing impact of conflicting data in DDFS by using second order knowledge
SETN'10 Proceedings of the 6th Hellenic conference on Artificial Intelligence: theories, models and applications
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So far few theoretical works investigated the conditions under which specific fusion rules can work well, and a unifying framework for comparing rules of different complexity is clearly beyond the state of the art. A clear theoretical comparison is lacking even if one focuses on specific classes of combiners (e.g., linear combiners). In this paper, we theoretically compare simple and weighted averaging rules for fusion of imbalanced classifiers. Continuing the work reported in [10], we get a deeper knowledge of classifiers imbalance effects in linear combiners. In addition, we experimentally compare the performance of linear and order statistics combiners for ensembles with different degrees of classifiers imbalance.