A Method of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition Letters
Multiple Classifier Combination Methodologies for Different Output Levels
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Experts' Boasting in Trainable Fusion Rules
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
k-nearest neighbors directed noise injection in multilayer perceptron training
IEEE Transactions on Neural Networks
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
Experts' Boasting in Trainable Fusion Rules
IEEE Transactions on Pattern Analysis and Machine Intelligence
Trainable fusion rules. I. Large sample size case
Neural Networks
Personal authentication using multiple palmprint representation
Pattern Recognition
Classifier combining rules under independence assumptions
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Reducing the overconfidence of base classifiers when combining their decisions
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
A new framework for adaptive multimodal biometrics management
IEEE Transactions on Information Forensics and Security
An experimental study of one- and two-level classifier fusion for different sample sizes
Pattern Recognition Letters
Cascade of fusion for adaptive classifier combination using context-awareness
AMDO'06 Proceedings of the 4th international conference on Articulated Motion and Deformable Objects
On deriving the second-stage training set for trainable combiners
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
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At present, fixed rules for classifier combination are the most used and widely investigated ones, while the study and application of trained rules has received much less attention. Therefore, pros and cons of fixed and trained rules are only partially known even if one focuses on crisp classifier outputs. In this paper, we report the results of an experimental comparison of well-known fixed and trained rules for crisp classifier outputs. Reported experiments allow one draw some preliminary conclusions about comparative advantages of fixed and trained fusion rules.