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Variable Ordering in the Conditional Independence Bayesian Classifier Induction Process: An Evolutionary Approach
Kaiserslautern, Germany September 17-September 19
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/HIS.2007.677th International Conference on Hybri ...
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Estevam R. Jr. Hruschka, Edimilson B. dos Santos, Sebastian D. C. de O. Galvao, "Variable Ordering in the Conditional Independence Bayesian Classifier Induction Process: An Evolutionary Approach," Hybrid Intelligent Systems, International Conference on, pp. 204-209, 7th International Conference on Hybrid Intelligent Systems (HIS 2007), 2007.
 
 
BibTex
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@article{ 10.1109/HIS.2007.67,
author = {Estevam R. Jr. Hruschka and Edimilson B. dos Santos and Sebastian D. C. de O. Galvao},
title = {Variable Ordering in the Conditional Independence Bayesian Classifier Induction Process: An Evolutionary Approach},
journal ={Hybrid Intelligent Systems, International Conference on},
volume = {0},
year = {2007},
isbn = {0-7695-2946-1},
pages = {204-209},
doi = {http://doi.ieeecomputersociety.org/10.1109/HIS.2007.67},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - Hybrid Intelligent Systems, International Conference on
TI - Variable Ordering in the Conditional Independence Bayesian Classifier Induction Process: An Evolutionary Approach
SN - 0-7695-2946-1
SP204
EP209
A1 - Estevam R. Jr. Hruschka,
A1 - Edimilson B. dos Santos,
A1 - Sebastian D. C. de O. Galvao,
PY - 2007
KW - null
VL - 0
JA - Hybrid Intelligent Systems, International Conference on
ER -
 
Estevam R. Jr. Hruschka, Universidade Federal de Sao Carlos, Brazil.
Edimilson B. dos Santos, Universidade Federal de Sao Carlos, Brazil.
Sebastian D. C. de O. Galvao, Universidade Federal de Sao Carlos, Brazil.
This work proposes, implements and discusses a hybrid Bayes/Genetic collaboration (VOGACMarkovPC) 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.
Citation:
Estevam R. Jr. Hruschka, Edimilson B. dos Santos, Sebastian D. C. de O. Galvao, "Variable Ordering in the Conditional Independence Bayesian Classifier Induction Process: An Evolutionary Approach," his, pp.204-209, 7th International Conference on Hybrid Intelligent Systems (HIS 2007), 2007
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