Weighted ensemble learning of Bayesian network for gene regulatory networks

Author:

Njah Hasna,Jamoussi Salma

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference75 articles.

1. K. Abu-Hakmeh, Assessing the use of voting methods to improve Bayesian network structure learning, (Master׳s thesis), School of Chemical & Biomolecular Engineering, December, 2012.

2. S. Aeberhard, D. Coomans, O. de Vel, Comparison of Classifiers in High Dimensional Settings, Technical Report 92-02, Department of Computer Science and Department of Mathematics and Statistics, James Cook University of North Queensland, 1992.

3. Optimizing fuzzy cluster ensemble in string representation;Alizadeh;Int. J. Pattern Recognit. Artif. Intell.,2013

4. Evolutionary approaches for the reverse-engineering of gene regulatory networks;Auliac;BMC Bioinform.,2008

5. I.A. Beinlich, H.J. Suermondt, R.M. Chavez, G.F. Cooper, The alarm monitoring system: A case study with two probabilistic inference techniques for belief networks. in: Proceedings of the 2nd European Conference on Artificial Intelligence in Medicine, Springer-Verlag, 1989, pp. 247–256.

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