Exploiting Reliability for Dynamic Selection of Classifiers by Means of Genetic Algorithms
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Selection of Classifiers for the Construction of Multiple Classifier Systems
ICDAR '05 Proceedings of the Eighth 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
Selection of classifiers using information-theoretic criteria
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
Information-Theoretic selection of classifiers for building multiple classifier systems
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
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Abstract: Only a few studies have been conducted on how to select multiple classifiers from the pool of available classifiers for showing the good performance. The selection problem of classifiers on how to select or how many to select still remains an important issue. In this paper, provided that the number of selected classifiers is constrained in advance, a number of selection criteria are proposed and applied to the construction of multiple classifiers. All the sets of classifiers are examined by the selection criteria under the constraint of the number of selected classifiers, and then some of those sets are selected as the candidates of multiple classifier systems. The multiple classifier system candidates were evaluated by the experiments recognizing UCI handwritten numerals.