The Community for Technology Leaders
2009 10th International Conference on Document Analysis and Recognition (2009)
Barcelona, Spain
July 26, 2009 to July 29, 2009
ISBN: 978-0-7695-3725-2
pp: 966-970
Combining classifier methods have shown their effectiveness in a number of applications. Nonetheless, using simultaneously multiple classifiers may result in some cases in a reduction of the overall performance, since the responses provided by some of the experts may generate consensus on a wrong decision even if other experts provided the correct one. To reduce these undesired effects, in a previous study, we proposed a combining method based on the use of a Bayesian Network. In this paper, we present an improvement of that method which allows to solve some of the drawbacks exhibited by standard learning algorithms for Bayesian Networks. The proposed method is based on an Evolutionary Algorithm which uses a specifically devised data structure to encode direct acyclic graphs. This data structure allows to effectively implement crossover and mutation operators. The experimental results, obtained by using three standard databases, confirmed the effectiveness of the method.
Classifier combination, Bayesian Networks, Evolutionary Algorithms

A. Marcelli, A. S. Freca, F. Fontanella and C. D. Stefano, "Learning Bayesian Networks by Evolution for Classifier Combination," 2009 10th International Conference on Document Analysis and Recognition(ICDAR), Barcelona, Spain, 2009, pp. 966-970.
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