Combination of Multiple Classifiers Using Local Accuracy Estimates
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining Classifiers Based on Minimization of a Bayes Error Rate
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
Experimental Results on the Construction of Multiple Classifiers Recognizing Handwritten Numerals
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Disturbing Neighbors Ensembles for Linear SVM
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
A Labelled Graph Based Multiple Classifier System
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
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Most studies on combining multiple classifiers have focused on combination methods, but a few studies have investigated on how to select component classifiers from a classifier pool. Multiple classifier systems performance varies with the component classifiers as well as the combination method. In this paper, methods based on information theory are proposed for selecting component classi- fiers, provided that the number of component classifiers is fixed in advance. These methods are applied to the classifier pool and examine the possible classifier sets. The system is compared to other multiple classifier systems on the recognition of unconstrained handwritten numerals.