Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Democracy in neural nets: voting schemes for classification
Neural Networks
Combination of Multiple Classifiers Using Local Accuracy Estimates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Lipreading: a classifier combination approach
Pattern Recognition Letters - special issue on pattern recognition in practice V
Adaptive confidence transform based classifier combination for Chinese character recognition
Pattern Recognition Letters
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Person Identification Using Multiple Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Classifier Conditional Posterior Probabilities
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Undesirable effects of output normalization in multiple classifier systems
Pattern Recognition Letters
Methods for Dynamic Classifier Selection
ICIAP '99 Proceedings of the 10th International Conference on Image Analysis and Processing
Combination of Face Classifiers for Person Identification
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
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Incomparability in classifier outputs due to the variability in their scales is a major problem in the combination of different classification systems. In order to compensate this, output normalization is generally performed where the main aim is to transform the outputs onto the same scale. In this paper, it is proposed that in selecting the transformation function, the scale similarity goal should be accomplished with two more requirements. The first one is the separability of the pattern classes in the transformed output space and the second is the compatibility of the outputs with the combination rule. A method of transformation that provides improved satisfaction of the additionalreq uirements is proposed which is shown to improve the classification performance of both linear and Bayesian combination systems based on the use of confusion matrix based a posteriori probabilities.