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
Evaluation of Combination Methods
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
On Combining Classifier Mass Functions for Text Categorization
IEEE Transactions on Knowledge and Data Engineering
Using neural network ensembles for bankruptcy prediction and credit scoring
Expert Systems with Applications: An International Journal
An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring
Expert Systems with Applications: An International Journal
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The performance of multiple classifier systems varieswith the performance of component classifiers as well asthe method of combination. In this paper, information-theoreticmethods are proposed for constructing multipleclassifier systems, provided that the number of componentclassifiers is constrained in advance. These proposed methodsare applied to a classifier pool and examine the possibleclassifier sets by the selected information-theoretic criteria.One of them is then selected as the candidate and isevaluated together with the other multiple classifier systemson the recognition of unconstrained handwritten numeralsfrom Concordia University and the University of California,Irvine. Experimental results support the approach.