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- W2158905978 abstract "This work proposes, implements and discusses a hybrid Bayes/genetic collaboration (VOGAC-MarkovPC) designed to induce conditional independence Bayesian classifiers from data. The main contribution is the use of MarkovPC algorithm in order to reduce the computational complexity of a genetic algorithm (GA) designed to explore the variable orderings (VOs) in order to optimize the induced classifiers. Experiments performed in a number of datasets revealed that VOGAC-MarkovPC required less than 25% of the time demanded by VOGAC-PC on average. In addition, when concerning the classification accuracy, VOGAC-MakovPC performed as well as VOGAC-PC did." @default.
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- W2158905978 date "2007-09-01" @default.
- W2158905978 modified "2023-10-16" @default.
- W2158905978 title "Variable Ordering in the Conditional Independence Bayesian Classifier Induction Process: An Evolutionary Approach" @default.
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- W2158905978 doi "https://doi.org/10.1109/his.2007.67" @default.
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