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
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
An Experimental Comparison of Fixed and Trained Fusion Rules for Crisp Classifier Outputs
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Media-Integrated Biometric Person Recognition Based on the Dempster-Shafer Theory
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Information fusion in biometrics
Pattern Recognition Letters - Special issue: Audio- and video-based biometric person authentication (AVBPA 2001)
Comparison and Combination of Ear and Face Images in Appearance-Based Biometrics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Large-Scale Evaluation of Multimodal Biometric Authentication Using State-of-the-Art Systems
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
A score-level fusion benchmark database for biometric authentication
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Fusion of face and speech data for person identity verification
IEEE Transactions on Neural Networks
Multi-domain sentiment classification with classifier combination
Journal of Computer Science and Technology - Special issue on natural language processing
Four machine learning algorithms for biometrics fusion: a comparative study
Applied Computational Intelligence and Soft Computing
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Classifier combining rules are designed for the fusion of the results from the component classifiers in a multiple classifier system. In this paper, we firstly propose a theoretical explanation of one important classifier combining rule, the sum rule, adopting the Bayes viewpoint under some independence assumptions. Our explanation is more general than what did in the existed previous by Kittler et al. [1]. Then, we present a new combining rule, named SumPro rule, which combines the sum rule with the product rule in a weighted average way. The weights for combining the two rules are tuned according to the development data using a genetic algorithm. The experimental evaluation and comparison among some combining rules are reported, which are done on a biometric authentication set. The results show that the SumPro rule takes a distinct advantage over both the sum rule and the product rule. Moreover, this new rule gradually outperforms the other popular trained combining rules when the classifier number increases.