Paper Info
Title
A Markov blanket based strategy to optimize the induction of Bayesian classifiers when using conditional independence learning algorithms
Abstract
A Bayesian Network (BN) is a multivariate joint probability distribution graphical representation that can be induced from data. The induction of a BN is a NP problem. Two main approaches can be used for inducing a BN from data, namely, Conditional Independence (CI) and the Heuristic Search (HS) based algorithms. When a BN is induced for classification purposes (Bayesian Classifier - BC), it is possible to impose some specific constraints aiming at an increase in computational efficiency. In this paper a new CI based algorithm (MarkovPC) to induce BCs from data is proposed. MarkovPC uses the Markov Blanket concept in order to impose some constraints and optimize the traditional PC algorithm. Experiments performed with ALARM BN, as well as other UCI and artificial domains revealed that MarkovPC tends to execute fewer comparisons than the traditional PC. The experiments also show that the MarkovPC produces competitive classification rates when compared with both, PC and Naïve Bayes.
Year
DOI
Venue
2007
10.1007/978-3-540-74553-2_33
DaWaK
Keywords
Field
DocType
competitive classification rate,bayesian network,conditional independence,traditional pc algorithm,traditional pc,bayesian classifier,alarm bn,new ci,classification purpose,heuristic search,markov blanket,bayesian networks,probability distribution
Data mining,Computer science,Markov blanket,Artificial intelligence,Bayesian statistics,Variable-order Bayesian network,Pattern recognition,Naive Bayes classifier,Algorithm,Bayesian network,Bayesian programming,Graphical model,Machine learning,Dynamic Bayesian network
Conference
Volume
ISSN
ISBN
4654
0302-9743
3-540-74552-1
Citations 
PageRank 
References 
3
0.47
6
Authors
2
Name
Order
Citations
PageRank
Sebastian D. C. de O. Galvão1101.66
Estevam R. Hruschka251044.97