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EnglishClassifier combination 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 paper, we proposed a combining method based on the use of a Bayesian Network. The structure of the Bayesian Network was learned by using an Evolutionary Algorithm which uses a specifically devised data structure to encode Direct Acyclic Graphs. In this paper we presents an further improvement along this direction, in that we have developed a new hybrid evolutionary algorithm in which the exploration of the search space has been improved by using a measure of the statistical dependencies among the experts. Moreover, new genetic operators have been defined that allow a more effective exploitation of the solutions in the evolving population. The experimental results, obtained by using two standard databases, confirmed the effectiveness of the method.
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| Titolo: | A Hybrid Evolutionary Algorithm for Bayesian Networks Learning: An Application to Classifier Combination. |
| Autori: | |
| Data di pubblicazione: | 2010 |
| Abstract: | Classifier combination 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 paper, we proposed a combining method based on the use of a Bayesian Network. The structure of the Bayesian Network was learned by using an Evolutionary Algorithm which uses a specifically devised data structure to encode Direct Acyclic Graphs. In this paper we presents an further improvement along this direction, in that we have developed a new hybrid evolutionary algorithm in which the exploration of the search space has been improved by using a measure of the statistical dependencies among the experts. Moreover, new genetic operators have been defined that allow a more effective exploitation of the solutions in the evolving population. The experimental results, obtained by using two standard databases, confirmed the effectiveness of the method. |
| Handle: | http://hdl.handle.net/11580/4026 |
| ISBN: | 9783642122385 |
| Appare nelle tipologie: | 2.1 Contributo in volume (Capitolo o Saggio) |
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