Combination of Multiple Classifiers Using Local Accuracy Estimates
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
An approach to the automatic design of multiple classifier systems
Pattern Recognition Letters - Special issue on machine learning and data mining in pattern recognition
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
Engineering multiversion neural-net systems
Neural Computation
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Combining multiple classifiers have focused mainly on combination methods, but a few studies have investigated on how to select component classifiers from a classifier pool. Performance by the information fusion varies with the component classifiers as well as the combination method. Previous studies focus on diverse classifiers which accurate and make different errors using the overproduce and choose strategy or the measures of diversity. In this paper, methods based on information theory are proposed for selecting component classifiers by considering the relationship among classifiers. These methods are applied to the classifier pool and examine the possible classifier sets. A classifier set is selected as a candidate and evaluated together with the other classifier sets on the recognition of public unconstrained handwritten numerals.