Bayesian Classifiers with Consensus Gene Selection: A Case Study in the Systemic Lupus Erythematosus
Within the wide field of classification on the Machine Learning discipline, Bayesian classifiers are very well established paradigms. They allow the user to work with probabilistic processes, as well as, with graphical representations of the relationships among the variables of a problem.
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- 1.R. Armañanzas. Solving bioinformatics problems by means of Bayesian classifiers and feature selection. Technical Report EHU-KZAA-IK-2/06, University of the Basque Country, 2006.Google Scholar
- 2.U. M. Fayyad and K. B. Irani. Multi-interval discretization of continuous-valued attributes for classification learning. In Proceedings of the Thirteenth Inter- national Joint Conference on Artificial Intelligence, pages 1022-1027. Morgan Kaufmann, 1993.Google Scholar
- 4.N. Friedman, M. Goldsmidt, and A. Wyner. Data analysis with Bayesian net- works: A bootstrap approach. In Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, pages 196-205, 1999.Google Scholar
- 5.M. A. Hall and L. A. Smith. Feature subset selection: A correlation based filter approach. In N. Kasabov et al., editor, Proceedings of the Fourth International Conference on Neural Information Processing and Intelligent Information Systems, pages 855-858, Dunedin, 1997.Google Scholar
- 7.M. Sahami. Learning limited dependence Bayesian classifiers. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, pages 335-338, 1996.Google Scholar