Non-similar candidate removal method for off-line handwritten Korean character recognition
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
A New Classifier Simulator for Evaluating Parallel Combination Methods
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Local voting of weak classifiers
International Journal of Knowledge-based and Intelligent Engineering Systems
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
Fusers Based on Classifier Response and Discriminant Function --- Comparative Study
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
Locally application of cascade generalization for classification problems
Intelligent Decision Technologies
Analysis of a Plurality Voting-based Combination of Classifiers
Neural Processing Letters
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
Logistic ensembles of Random Spherical Linear Oracles for microarray classification
International Journal of Data Mining and Bioinformatics
Learning Visual Object Categories with Global Descriptors and Local Features
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Parameterized complexity of control problems in Maximin election
Information Processing Letters
Cascade generalisation for ordinal problems
International Journal of Artificial Intelligence and Soft Computing
Simulating classifier outputs for evaluating parallel combination methods
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Combining classifier with a fuser implemented as a one layer perceptron
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part II
Noisy digit classification with multiple specialist
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
Positional and confidence voting-based consensus functions for fuzzy cluster ensembles
Fuzzy Sets and Systems
Multiple classifier systems for embedded string patterns
ANNPR'06 Proceedings of the Second international conference on Artificial Neural Networks in Pattern Recognition
Cut digits classification with k-NN multi-specialist
DAS'06 Proceedings of the 7th international conference on Document Analysis Systems
Double fusion for multimedia event detection
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
Combining diverse one-class classifiers
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
A multiclassifier approach for drill wear prediction
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
Classifier ensemble for an effective cytological image analysis
Pattern Recognition Letters
A survey of multiple classifier systems as hybrid systems
Information Fusion
Cost-sensitive decision tree ensembles for effective imbalanced classification
Applied Soft Computing
Clustering-based ensembles for one-class classification
Information Sciences: an International Journal
Feature ranking fusion for text classifier
Intelligent Data Analysis
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In pattern recognition, there is a growing use of multiple classifier combinations with the goal to increase recognition performance. In many cases, plurality voting is a part of the combination process. In this article, we discuss and test several well known voting methods from politicsand economics on classifier combination in order to see if an alternative to the simple plurality vote exists. We found that, assuming a number of prerequisites, better methods are available, that are comparatively simple and fast.