Learning Bayesian Networks in the Space of Orderings With Estimation of Distribution Algorithms

Romero, Txomin; Larrañaga, Pedro; Sierra, Basilio

doi:10.1142/s0218001404003332

Cited by 25 publications

(11 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This assumption dramatically reduces the cardinality of the search to n !. Seminal works include Singh and Valtorta ( 1993 ) using conditional independence tests, Bouckaert ( 1992 ) who manipulates the ordering of the variables with operations similar to arc reversals, Larrañaga et al ( 1996a ) with a genetic algorithm-based search, and Romero et al ( 2004 ) using estimation of distribution algorithms.…”

Section: Learning Bayesian Network From Datamentioning

confidence: 99%

Bayesian networks in neuroscience: a survey

Bielza

Larrañaga

2014

Front. Comput. Neurosci.

129

View full text Add to dashboard Cite

Bayesian networks are a type of probabilistic graphical models lie at the intersection between statistics and machine learning. They have been shown to be powerful tools to encode dependence relationships among the variables of a domain under uncertainty. Thanks to their generality, Bayesian networks can accommodate continuous and discrete variables, as well as temporal processes. In this paper we review Bayesian networks and how they can be learned automatically from data by means of structure learning algorithms. Also, we examine how a user can take advantage of these networks for reasoning by exact or approximate inference algorithms that propagate the given evidence through the graphical structure. Despite their applicability in many fields, they have been little used in neuroscience, where they have focused on specific problems, like functional connectivity analysis from neuroimaging data. Here we survey key research in neuroscience where Bayesian networks have been used with different aims: discover associations between variables, perform probabilistic reasoning over the model, and classify new observations with and without supervision. The networks are learned from data of any kind–morphological, electrophysiological, -omics and neuroimaging–, thereby broadening the scope–molecular, cellular, structural, functional, cognitive and medical– of the brain aspects to be studied.

show abstract

Section: Learning Bayesian Network From Datamentioning

confidence: 99%

Bayesian networks in neuroscience: a survey

Bielza

Larrañaga

2014

Front. Comput. Neurosci.

129

View full text Add to dashboard Cite

show abstract

“…In [46] authors use two versions of EDA namely UMDA and MIMIC to obtain the VO for the K2 algorithm. Works that use VOs to learn BNs from data, which are not based on EA approaches, can be found in [46] and references therein.…”

Section: A View Into Work Related To Evolutionary Algorithms and Baymentioning

confidence: 99%

The influences of canonical evolutionary algorithm operators and variable orderings in learning Bayesian classifiers from data

Santos

Hruschka

Nicoletti

et al. 2014

HIS

View full text Add to dashboard Cite

Variable Ordering (VO) plays an important role when inducing Bayesian Networks (BNs) and Bayesian Classifiers (BCs). Previous works in the literature suggest that it is worth pursuing the use of genetic/evolutionary algorithms for identifying a suitable VO, when learning a BN structure from data. This paper proposes a collaborative Evolutionary-Bayes algorithm named VOEA (Variable Ordering Evolutionary Algorithm) aimed at inducing BCs from data. The two VOEA versions presented in the paper refine a previously proposed algorithm named VOGA by employing only a single evolutionary operator (either crossover or mutation) as well as by using information about the class variable when defining the most suitable variable ordering for learning a BC. Experiments performed in a number of datasets revealed that the VOEA approach is promising and tends to generate suitable and representative BCs, particularly in its version VOEA_M, which only implements the mutation operator.

show abstract

“…In (Romero et al, 2004), two approaches (UMDA and MIMIC) have been applied to the topological orders space. Individuals (i.e.…”

Section: Wwwintechopencommentioning

confidence: 99%

Design of Evolutionary Methods Applied to the Learning of Bayesian Network Structures

Brouard¹,

Delaplace²,

Luqman³

et al. 2010

Bayesian Network

View full text Add to dashboard Cite

Learning Bayesian Networks in the Space of Orderings With Estimation of Distribution Algorithms

Cited by 25 publications

References 15 publications

Bayesian networks in neuroscience: a survey

Bayesian networks in neuroscience: a survey

The influences of canonical evolutionary algorithm operators and variable orderings in learning Bayesian classifiers from data

Design of Evolutionary Methods Applied to the Learning of Bayesian Network Structures

Contact Info

Product

Resources

About