A Hybrid Evolutionary Algorithm for Bayesian Networks Learning: An Application to Classifier Combination
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 a 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.
KeywordsMutual Information Bayesian Network Direct Acyclic Graph Statistical Dependency Genetic Operator
Unable to display preview. Download preview PDF.
- 1.Chickering, D.M., Geiger, D., Heckerman, D.: Learning bayesian networks is np-hard. Tech. rep. (1994)Google Scholar
- 5.De Stefano, C., Fontanella, F., Marcelli, A., Scotto di Freca, A.: Learning bayesian networks by evolution for classifier combination. In: ICDAR 2009: Proceedings of the 2009 10th International Conference on Document Analysis and Recognition, pp. 966–970. IEEE Computer Society, Los Alamitos (2009)Google Scholar
- 7.Heckerman, D.: A tutorial on learning with bayesian networks. Tech. rep., Learning in Graphical Models (1995)Google Scholar
- 10.Kohonen, T.: Self organizing map. Springer, Berlin (1995)Google Scholar
- 13.Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Francisco (1988)Google Scholar