Sixth International Conference on Hybrid Intelligent Systems (HIS'06) VOGA: Variable Ordering Genetic Algorithm for Learning Bayesian Classifiers Auckland, New Zealand December 13-December 15 ISBN: 0-7695-2662-4
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/HIS.2006.77
This work proposes a hybrid approach to help the process of learning a Bayesian Classifier (BC) from data. The proposed method named VOGA (and its variant VOGA+) uses a Genetic Algorithm to optimize the BC learning process by means of the identification of an adequate variables ordering. The main contribution of VOGA and VOGA+ is the use information about the class variable when defining the most suitable variable ordering. Trying to optimize the GA initial population, VOGA+ ranks the attributes based on the class variable. Experiments performed in a number of datasets revealed that both methods are promising and VOGA+ tends to be favored domains having higher number of variables.
Citation:
Edimilson Batista dos Santos, Estevam Rafael Hruschka Junior, "VOGA: Variable Ordering Genetic Algorithm for Learning Bayesian Classifiers," his, pp.56, Sixth International Conference on Hybrid Intelligent Systems (HIS'06), 2006 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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