Paper Info
Title
Variable Ordering in the Conditional Independence Bayesian Classifier Induction Process: An Evolutionary Approach
Abstract
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.
Year
DOI
Venue
2007
10.1109/HIS.2007.74
HIS
Keywords
Field
DocType
conditional independence,computational complexity,bayesian classifier,markov processes,genetics,genetic algorithm,genetic algorithms
Data mining,Markov process,Naive Bayes classifier,Conditional independence,Computer science,Artificial intelligence,Genetic algorithm,Machine learning,Bayes' theorem,Bayesian probability,Computational complexity theory
Conference
ISBN
Citations 
PageRank 
0-7695-2946-1
1
0.35
References 
Authors
3
3
Name
Order
Citations
PageRank
Estevam R. Hruschka151044.97
Edimilson Batista Dos Santos2142.84
Sebastian D. C. de O. Galvão3101.66