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A Hybrid Evolutionary Algorithm for Bayesian Networks Learning: An Application to Classifier Combination

  • Claudio De Stefano
  • Francesco Fontanella
  • Cristina Marrocco
  • Alessandra Scotto di Freca
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6024)

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 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.

Keywords

Mutual Information Bayesian Network Direct Acyclic Graph Statistical Dependency Genetic Operator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Claudio De Stefano
    • 1
  • Francesco Fontanella
    • 1
  • Cristina Marrocco
    • 1
  • Alessandra Scotto di Freca
    • 1
  1. 1.Università di CassinoCassinoItaly

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